1. Overview of Project

Insert Abstract

2. Introduction of the file

The following document contains results from all analyses conducted for the manuscript titled “Trajectories of Risk-taking Propensity: A Coordinated Analysis of Longitudinal Panels”. This document is organized by different domain risk-taking propensity,including general, driving, financial, recreational, occupational, health and social domain. For each risk-taking propensity, we create 7 models (including intercept-only model, fixed effect model, linear model, linear with gender model, linear with gender interaction model, quadratic model and quadratic with gender model) and provide a table summarizing individual study model results, trajectory plots, the meta-analysis results. We tested individual predictors that are not included in the simple trajectory model in meta regression: country, continent, mean age and scale range. And the results from these models are available below. The code used to compile this file is available here (insert link)

3. Overview of panel data

3.1. The number of participants

3.2. Histogram of age distributions (all observations)

3.3. Histograms and Density Plots for every panel

SOEP

Age distribution

Risk density

HRS

Age distribution

Risk density

SAVE

Age distribution

Risk density

DNB

Age distribution

Risk density

PHF

Age distribution

Risk density

SHARE_Austria

Age distribution

Risk density

SHARE_Germany

Age distribution

Risk density

SHARE_Sweden

Age distribution

Risk density

SHARE_Netherlands

Age distribution

Risk density

SHARE_Spain

Age distribution

Risk density

SHARE_Italy

Age distribution

Risk density

SHARE_France

Age distribution

Risk density

SHARE_Denmark

Age distribution

Risk density

SHARE_Switzerland

Age distribution

Risk density

SHARE_Belgium

Age distribution

Risk density

SHARE_Israel

Age distribution

Risk density

SHARE_Czech_Republic

Age distribution

Risk density

SHARE_Slovenia

Age distribution

Risk density

SHARE_Estonia

Age distribution

Risk density

Usoc

Age distribution

Risk density

GCOE_Japan

Age distribution

Risk density

GCOE_USA

Age distribution

Risk density

HILDA

Age distribution

Risk density

LIKS

Age distribution

Risk density

4. Multi-level model results

4.1. General risk-taking

Intercept only model

Models results

Fixed effect model

Models results

Linear model

Models results
Plot

Linear with gender model

Models results
Plot

Linear with gender interaction model

Models results
Plot

Quadratic model

Models results
Plot

Quadratic with gender model

Models results
Plot

4.2. Driving risk-taking

Intercept only model

Models results

Fixed effect model

Models results

Linear model

Models results
Plot

Linear with gender model

Models results
Plot

Linear with gender interaction model

Models results
Plot

4.3. Financial risk-taking

Intercept only model

Models results

Fixed effect model

Models results

Linear model

Models results
Plot

Linear with gender model

Models results
Plot

Linear with gender interaction model

Models results
Plot

Quadratic model

Models results
Plot

Quadratic with gender model

Models results
Plot

4.4. Recreational risk-taking

Intercept only model

Models results

Fixed effect model

Models results

Linear model

Models results
Plot

Linear with gender model

Models results
Plot

Linear with gender interaction model

Models results
Plot

4.5. Occupational risk-taking

Intercept only model

Models results

Fixed effect model

Models results

Linear model

Models results
Plot

Linear with gender model

Models results
Plot

Linear with gender interaction model

Models results
Plot

4.6. Health risk-taking

Intercept only model

Models results

Fixed effect model

Models results

Linear model

Models results
Plot

Linear with gender model

Models results
Plot

Linear with gender interaction model

Models results
Plot

4.7. Social risk-taking

5. Meta-analysis results

5.1. General risk-taking

Intercept only model

Meta analysis
ICC’s results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.9766   -7.9532   -3.9532   -4.3697    0.0468   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value):      0.1246
## I^2 (total heterogeneity / total variability):   99.83%
## H^2 (total variability / sampling variability):  582.81
## 
## Test for Heterogeneity:
## Q(df = 6) = 3633.8026, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4330  0.0472  9.1660  <.0001  0.3404  0.5256  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
ICC’s results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.9112   -5.8225    2.1775   -0.2773   42.1775   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0136 (SE = 0.0097)
## tau (square root of estimated tau^2 value):             0.1166
## I^2 (residual heterogeneity / unaccounted variability): 99.68%
## H^2 (unaccounted variability / sampling variability):   316.66
## R^2 (amount of heterogeneity accounted for):            12.31%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 1457.3211, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.8299, p-val = 0.2429
## 
## Model Results:
## 
##                         estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt                   0.3250  0.0826  3.9351  <.0001   0.1631  0.4868  *** 
## continentEurope           0.1798  0.1069  1.6821  0.0926  -0.0297  0.3892    . 
## continentNorth America    0.1096  0.1168  0.9383  0.3481  -0.1193  0.3385      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
ICC’s results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.6056   -7.2112   -1.2112   -2.3829   22.7888   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0138 (SE = 0.0088)
## tau (square root of estimated tau^2 value):             0.1175
## I^2 (residual heterogeneity / unaccounted variability): 99.80%
## H^2 (unaccounted variability / sampling variability):   490.35
## R^2 (amount of heterogeneity accounted for):            11.08%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 3487.6720, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7437, p-val = 0.1867
## 
## Model Results:
## 
##           estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt     0.0226  0.3139  0.0721  0.9425  -0.5926  0.6379    
## mean.age    0.0080  0.0061  1.3205  0.1867  -0.0039  0.0199    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
ICC’s results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.9766   -7.9532   -3.9532   -4.3697    0.0468   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value):      0.1246
## I^2 (total heterogeneity / total variability):   99.83%
## H^2 (total variability / sampling variability):  582.81
## 
## Test for Heterogeneity:
## Q(df = 6) = 3633.8026, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4330  0.0472  9.1660  <.0001  0.3404  0.5256  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.5846  -23.1692  -19.1692  -19.5857  -15.1692   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0321
## I^2 (total heterogeneity / total variability):   97.81%
## H^2 (total variability / sampling variability):  45.72
## 
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0767  0.0126  -6.1045  <.0001  -0.1014  -0.0521  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.4638  -14.9277   -6.9277   -9.3825   33.0723   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value):             0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.97%
## H^2 (unaccounted variability / sampling variability):   32.99
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 109.9605, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4546, p-val = 0.4832
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.0837  0.0253  -3.3138  0.0009  -0.1332  -0.0342 
## continentEurope          -0.0066  0.0330  -0.1998  0.8416  -0.0712   0.0580 
## continentNorth America    0.0316  0.0356   0.8892  0.3739  -0.0381   0.1013 
##  
## intrcpt                 *** 
## continentEurope 
## continentNorth America 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.3843  -18.7685  -12.7685  -13.9402   11.2315   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0012 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0341
## I^2 (residual heterogeneity / unaccounted variability): 98.09%
## H^2 (unaccounted variability / sampling variability):   52.33
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 195.1708, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5208, p-val = 0.4705
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1430  0.0925  -1.5464  0.1220  -0.3243  0.0382    
## mean.age    0.0013  0.0018   0.7217  0.4705  -0.0022  0.0048    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.5846  -23.1692  -19.1692  -19.5857  -15.1692   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0321
## I^2 (total heterogeneity / total variability):   97.81%
## H^2 (total variability / sampling variability):  45.72
## 
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0767  0.0126  -6.1045  <.0001  -0.1014  -0.0521  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.5819  -23.1638  -19.1638  -19.5803  -15.1638   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0321
## I^2 (total heterogeneity / total variability):   97.75%
## H^2 (total variability / sampling variability):  44.45
## 
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0767  0.0126  -6.1043  <.0001  -0.1013  -0.0521  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.4614  -14.9228   -6.9228   -9.3776   33.0772   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value):             0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.90%
## H^2 (unaccounted variability / sampling variability):   32.30
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 104.0509, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4584, p-val = 0.4823
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.0843  0.0252  -3.3417  0.0008  -0.1337  -0.0349 
## continentEurope          -0.0055  0.0329  -0.1680  0.8666  -0.0701   0.0590 
## continentNorth America    0.0324  0.0355   0.9119  0.3618  -0.0372   0.1020 
##  
## intrcpt                 *** 
## continentEurope 
## continentNorth America 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.3990  -18.7981  -12.7981  -13.9698   11.2019   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0012 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0340
## I^2 (residual heterogeneity / unaccounted variability): 98.01%
## H^2 (unaccounted variability / sampling variability):   50.33
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 176.8991, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5606, p-val = 0.4540
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1451  0.0921  -1.5763  0.1150  -0.3255  0.0353    
## mean.age    0.0013  0.0018   0.7487  0.4540  -0.0022  0.0048    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.5819  -23.1638  -19.1638  -19.5803  -15.1638   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0321
## I^2 (total heterogeneity / total variability):   97.75%
## H^2 (total variability / sampling variability):  44.45
## 
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0767  0.0126  -6.1043  <.0001  -0.1013  -0.0521  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.2626  -22.5253  -18.5253  -18.9418  -14.5253   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0342
## I^2 (total heterogeneity / total variability):   98.07%
## H^2 (total variability / sampling variability):  51.88
## 
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0775  0.0134  -5.8080  <.0001  -0.1037  -0.0514  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.6895   -9.3790   -5.3790   -5.7955   -1.3790   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value):      0.1086
## I^2 (total heterogeneity / total variability):   98.21%
## H^2 (total variability / sampling variability):  55.78
## 
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2392  0.0424  -5.6470  <.0001  -0.3222  -0.1562  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.2889  -14.5777   -6.5777   -9.0325   33.4223   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0013 (SE = 0.0010)
## tau (square root of estimated tau^2 value):             0.0367
## I^2 (residual heterogeneity / unaccounted variability): 97.23%
## H^2 (unaccounted variability / sampling variability):   36.10
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 107.9715, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.5427, p-val = 0.4624
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.0868  0.0265  -3.2810  0.0010  -0.1387  -0.0350  ** 
## continentEurope          -0.0040  0.0345  -0.1168  0.9070  -0.0717   0.0636     
## continentNorth America    0.0363  0.0373   0.9738  0.3302  -0.0368   0.1094     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.1904   -6.3808    1.6192   -0.8356   41.6192   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0114 (SE = 0.0087)
## tau (square root of estimated tau^2 value):             0.1067
## I^2 (residual heterogeneity / unaccounted variability): 96.98%
## H^2 (unaccounted variability / sampling variability):   33.08
## R^2 (amount of heterogeneity accounted for):            3.46%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 178.2441, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.1733, p-val = 0.3373
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.3311  0.0768  -4.3112  <.0001  -0.4816  -0.1806 
## continentEurope           0.1143  0.1008   1.1338  0.2569  -0.0833   0.3118 
## continentNorth America    0.1521  0.1082   1.4057  0.1598  -0.0600   0.3642 
##  
## intrcpt                 *** 
## continentEurope 
## continentNorth America 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.0671  -18.1342  -12.1342  -13.3059   11.8658   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0014 (SE = 0.0009)
## tau (square root of estimated tau^2 value):             0.0368
## I^2 (residual heterogeneity / unaccounted variability): 98.35%
## H^2 (unaccounted variability / sampling variability):   60.47
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 195.2758, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3968, p-val = 0.5288
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1399  0.0996  -1.4050  0.1600  -0.3350  0.0553    
## mean.age    0.0012  0.0019   0.6299  0.5288  -0.0026  0.0050    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.6041   -7.2083   -1.2083   -2.3800   22.7917   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0134 (SE = 0.0090)
## tau (square root of estimated tau^2 value):             0.1156
## I^2 (residual heterogeneity / unaccounted variability): 98.26%
## H^2 (unaccounted variability / sampling variability):   57.38
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 233.8725, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3336, p-val = 0.5635
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.4172  0.3113  -1.3400  0.1802  -1.0274  0.1930    
## mean.age    0.0035  0.0060   0.5776  0.5635  -0.0083  0.0153    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.2626  -22.5253  -18.5253  -18.9418  -14.5253   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0342
## I^2 (total heterogeneity / total variability):   98.07%
## H^2 (total variability / sampling variability):  51.88
## 
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0775  0.0134  -5.8080  <.0001  -0.1037  -0.0514  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.6895   -9.3790   -5.3790   -5.7955   -1.3790   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value):      0.1086
## I^2 (total heterogeneity / total variability):   98.21%
## H^2 (total variability / sampling variability):  55.78
## 
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2392  0.0424  -5.6470  <.0001  -0.3222  -0.1562  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender interaction model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  10.4637  -20.9274  -16.9274  -17.3438  -12.9274   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value):      0.0363
## I^2 (total heterogeneity / total variability):   96.41%
## H^2 (total variability / sampling variability):  27.87
## 
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0806  0.0145  -5.5610  <.0001  -0.1091  -0.0522  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.6721   -9.3442   -5.3442   -5.7607   -1.3442   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value):      0.1087
## I^2 (total heterogeneity / total variability):   97.61%
## H^2 (total variability / sampling variability):  41.77
## 
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2311  0.0425  -5.4373  <.0001  -0.3144  -0.1478  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  13.9104  -27.8209  -23.8209  -24.2373  -19.8209   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value):      0.0190
## I^2 (total heterogeneity / total variability):   79.78%
## H^2 (total variability / sampling variability):  4.94
## 
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0074  0.0088  0.8469  0.3971  -0.0098  0.0246    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   6.5136  -13.0272   -5.0272   -7.4820   34.9728   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0018 (SE = 0.0014)
## tau (square root of estimated tau^2 value):             0.0422
## I^2 (residual heterogeneity / unaccounted variability): 95.69%
## H^2 (unaccounted variability / sampling variability):   23.19
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 64.1273, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.0897, p-val = 0.5799
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.0940  0.0308  -3.0520  0.0023  -0.1544  -0.0336  ** 
## continentEurope           0.0020  0.0404   0.0496  0.9604  -0.0771   0.0811     
## continentNorth America    0.0391  0.0433   0.9028  0.3666  -0.0458   0.1241     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.0362   -6.0724    1.9276   -0.5272   41.9276   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0123 (SE = 0.0094)
## tau (square root of estimated tau^2 value):             0.1109
## I^2 (residual heterogeneity / unaccounted variability): 96.47%
## H^2 (unaccounted variability / sampling variability):   28.30
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 174.4541, p-val < .0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.7403, p-val = 0.4189
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.3140  0.0798  -3.9350  <.0001  -0.4704  -0.1576 
## continentEurope           0.0975  0.1046   0.9318  0.3515  -0.1075   0.3025 
## continentNorth America    0.1455  0.1129   1.2890  0.1974  -0.0757   0.3667 
##  
## intrcpt                 *** 
## continentEurope 
## continentNorth America 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0004 (SE = 0.0004)
## tau (square root of estimated tau^2 value):             0.0194
## I^2 (residual heterogeneity / unaccounted variability): 71.00%
## H^2 (unaccounted variability / sampling variability):   3.45
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 4) = 11.7950, p-val = 0.0189
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.9695, p-val = 0.6158
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                   0.0188  0.0166   1.1344  0.2566  -0.0137  0.0513    
## continentEurope          -0.0212  0.0218  -0.9736  0.3302  -0.0640  0.0215    
## continentNorth America   -0.0095  0.0230  -0.4111  0.6810  -0.0546  0.0357    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   8.7135  -17.4271  -11.4271  -12.5987   12.5729   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0013 (SE = 0.0009)
## tau (square root of estimated tau^2 value):             0.0358
## I^2 (residual heterogeneity / unaccounted variability): 96.36%
## H^2 (unaccounted variability / sampling variability):   27.49
## R^2 (amount of heterogeneity accounted for):            3.07%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 69.9653, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2655, p-val = 0.2606
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1900  0.0984  -1.9317  0.0534  -0.3829  0.0028  . 
## mean.age    0.0021  0.0019   1.1249  0.2606  -0.0016  0.0059    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.5750   -7.1500   -1.1500   -2.3217   22.8500   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0135 (SE = 0.0091)
## tau (square root of estimated tau^2 value):             0.1161
## I^2 (residual heterogeneity / unaccounted variability): 97.94%
## H^2 (unaccounted variability / sampling variability):   48.60
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 243.5127, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2861, p-val = 0.5928
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.3982  0.3154  -1.2624  0.2068  -1.0164  0.2200    
## mean.age    0.0033  0.0061   0.5348  0.5928  -0.0087  0.0152    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0144
## I^2 (residual heterogeneity / unaccounted variability): 69.66%
## H^2 (unaccounted variability / sampling variability):   3.30
## R^2 (amount of heterogeneity accounted for):            42.24%
## 
## Test for Residual Heterogeneity:
## QE(df = 5) = 18.0249, p-val = 0.0029
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.5823, p-val = 0.0584
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.0971  0.0479   2.0287  0.0425   0.0033  0.1909  * 
## mean.age   -0.0018  0.0009  -1.8927  0.0584  -0.0036  0.0001  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  10.4637  -20.9274  -16.9274  -17.3438  -12.9274   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value):      0.0363
## I^2 (total heterogeneity / total variability):   96.41%
## H^2 (total variability / sampling variability):  27.87
## 
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0806  0.0145  -5.5610  <.0001  -0.1091  -0.0522  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.6721   -9.3442   -5.3442   -5.7607   -1.3442   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value):      0.1087
## I^2 (total heterogeneity / total variability):   97.61%
## H^2 (total variability / sampling variability):  41.77
## 
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2311  0.0425  -5.4373  <.0001  -0.3144  -0.1478  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  13.9104  -27.8209  -23.8209  -24.2373  -19.8209   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value):      0.0190
## I^2 (total heterogeneity / total variability):   79.78%
## H^2 (total variability / sampling variability):  4.94
## 
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0074  0.0088  0.8469  0.3971  -0.0098  0.0246    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Quadratic model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.2079  -14.4159  -10.4159  -12.2186    1.5841   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0213
## I^2 (total heterogeneity / total variability):   96.04%
## H^2 (total variability / sampling variability):  25.25
## 
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0838  0.0110  -7.5977  <.0001  -0.1054  -0.0622  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   8.6378  -17.2756  -13.2756  -15.0784   -1.2756   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value):      0.0133
## I^2 (total heterogeneity / total variability):   97.33%
## H^2 (total variability / sampling variability):  37.51
## 
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0017  0.0069  -0.2438  0.8074  -0.0152  0.0118    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.8784   -7.7569    0.2431   -7.7569   40.2431   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0001)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1527, p-val = 0.6959
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 63.4417, p-val < .0001
## 
## Model Results:
## 
##                         estimate      se      zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.0949  0.0039  -24.5103  <.0001  -0.1025  -0.0873 
## continentEurope          -0.0019  0.0044   -0.4375  0.6617  -0.0104   0.0066 
## continentNorth America    0.0434  0.0066    6.5634  <.0001   0.0305   0.0564 
##  
## intrcpt                 *** 
## continentEurope 
## continentNorth America  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.5526   -5.1053    2.8947   -5.1053   42.8947   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0003 (SE = 0.0005)
## tau (square root of estimated tau^2 value):             0.0182
## I^2 (residual heterogeneity / unaccounted variability): 93.04%
## H^2 (unaccounted variability / sampling variability):   14.38
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.3760, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5824, p-val = 0.7474
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                  -0.0076  0.0133  -0.5729  0.5667  -0.0337  0.0185    
## continentEurope           0.0172  0.0225   0.7630  0.4454  -0.0270  0.0614    
## continentNorth America    0.0063  0.0227   0.2778  0.7812  -0.0382  0.0508    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.4296   -8.8591   -2.8591   -6.7797   21.1409   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0007 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0259
## I^2 (residual heterogeneity / unaccounted variability): 96.44%
## H^2 (unaccounted variability / sampling variability):   28.06
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 63.5800, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0411, p-val = 0.8393
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1155  0.1571  -0.7353  0.4621  -0.4235  0.1924    
## mean.age    0.0007  0.0033   0.2028  0.8393  -0.0058  0.0071    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.3794  -14.7588   -8.7588  -12.6793   15.2412   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0000 (SE = 0.0000)
## tau (square root of estimated tau^2 value):             0.0052
## I^2 (residual heterogeneity / unaccounted variability): 78.35%
## H^2 (unaccounted variability / sampling variability):   4.62
## R^2 (amount of heterogeneity accounted for):            84.57%
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 9.8418, p-val = 0.0073
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 14.3505, p-val = 0.0002
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.1404  0.0366  -3.8364  0.0001  -0.2121  -0.0687  *** 
## mean.age    0.0030  0.0008   3.7882  0.0002   0.0014   0.0045  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.2079  -14.4159  -10.4159  -12.2186    1.5841   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0213
## I^2 (total heterogeneity / total variability):   96.04%
## H^2 (total variability / sampling variability):  25.25
## 
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0838  0.0110  -7.5977  <.0001  -0.1054  -0.0622  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   8.6378  -17.2756  -13.2756  -15.0784   -1.2756   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value):      0.0133
## I^2 (total heterogeneity / total variability):   97.33%
## H^2 (total variability / sampling variability):  37.51
## 
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0017  0.0069  -0.2438  0.8074  -0.0152  0.0118    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Quadratic with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   6.9518  -13.9036   -9.9036  -11.7064    2.0964   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value):      0.0231
## I^2 (total heterogeneity / total variability):   96.71%
## H^2 (total variability / sampling variability):  30.38
## 
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0842  0.0119  -7.0709  <.0001  -0.1075  -0.0608  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   8.8141  -17.6282  -13.6282  -15.4310   -1.6282   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0125
## I^2 (total heterogeneity / total variability):   97.09%
## H^2 (total variability / sampling variability):  34.37
## 
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0019  0.0065  -0.2928  0.7697  -0.0147  0.0109    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.9033   -5.8067   -1.8067   -3.6095   10.1933   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value):      0.0900
## I^2 (total heterogeneity / total variability):   97.85%
## H^2 (total variability / sampling variability):  46.60
## 
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2864  0.0458  -6.2497  <.0001  -0.3762  -0.1966  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.5250   -7.0500    0.9500   -7.0500   40.9500   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0001)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8964, p-val = 0.3437
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 68.8365, p-val < .0001
## 
## Model Results:
## 
##                         estimate      se      zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.0926  0.0038  -24.3258  <.0001  -0.1000  -0.0851 
## continentEurope          -0.0039  0.0043   -0.9013  0.3674  -0.0122   0.0045 
## continentNorth America    0.0434  0.0066    6.6023  <.0001   0.0305   0.0563 
##  
## intrcpt                 *** 
## continentEurope 
## continentNorth America  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.6318   -5.2637    2.7363   -5.2637   42.7363   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value):             0.0167
## I^2 (residual heterogeneity / unaccounted variability): 92.13%
## H^2 (unaccounted variability / sampling variability):   12.70
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.7009, p-val = 0.0004
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.6519, p-val = 0.7218
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                  -0.0078  0.0123  -0.6330  0.5267  -0.0319  0.0163    
## continentEurope           0.0168  0.0208   0.8071  0.4196  -0.0239  0.0574    
## continentNorth America    0.0063  0.0209   0.3018  0.7628  -0.0347  0.0473    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1327   -4.2654    3.7346   -4.2654   43.7346   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0004 (SE = 0.0012)
## tau (square root of estimated tau^2 value):             0.0202
## I^2 (residual heterogeneity / unaccounted variability): 49.47%
## H^2 (unaccounted variability / sampling variability):   1.98
## R^2 (amount of heterogeneity accounted for):            94.98%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.9790, p-val = 0.1595
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 34.2919, p-val < .0001
## 
## Model Results:
## 
##                         estimate      se      zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.3235  0.0191  -16.9022  <.0001  -0.3610  -0.2860 
## continentEurope          -0.0117  0.0287   -0.4071  0.6839  -0.0679   0.0446 
## continentNorth America    0.1730  0.0331    5.2319  <.0001   0.1082   0.2378 
##  
## intrcpt                 *** 
## continentEurope 
## continentNorth America  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.2380   -8.4760   -2.4760   -6.3965   21.5240   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0008 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0285
## I^2 (residual heterogeneity / unaccounted variability): 97.10%
## H^2 (unaccounted variability / sampling variability):   34.46
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 68.6838, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0132, p-val = 0.9084
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0645  0.1722  -0.3747  0.7079  -0.4020  0.2730    
## mean.age   -0.0004  0.0036  -0.1150  0.9084  -0.0075  0.0067    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.4026  -14.8053   -8.8053  -12.7259   15.1947   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0000 (SE = 0.0000)
## tau (square root of estimated tau^2 value):             0.0051
## I^2 (residual heterogeneity / unaccounted variability): 77.91%
## H^2 (unaccounted variability / sampling variability):   4.53
## R^2 (amount of heterogeneity accounted for):            83.30%
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 9.5913, p-val = 0.0083
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 13.1657, p-val = 0.0003
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.1321  0.0359  -3.6819  0.0002  -0.2025  -0.0618  *** 
## mean.age    0.0028  0.0008   3.6285  0.0003   0.0013   0.0043  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5391   -3.0783    2.9217   -0.9988   26.9217   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0122 (SE = 0.0125)
## tau (square root of estimated tau^2 value):             0.1106
## I^2 (residual heterogeneity / unaccounted variability): 98.08%
## H^2 (unaccounted variability / sampling variability):   52.12
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 2) = 92.6361, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0213, p-val = 0.8839
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1902  0.6615  -0.2875  0.7737  -1.4867  1.1063    
## mean.age   -0.0020  0.0139  -0.1460  0.8839  -0.0293  0.0252    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   6.9518  -13.9036   -9.9036  -11.7064    2.0964   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value):      0.0231
## I^2 (total heterogeneity / total variability):   96.71%
## H^2 (total variability / sampling variability):  30.38
## 
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0842  0.0119  -7.0709  <.0001  -0.1075  -0.0608  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   8.8141  -17.6282  -13.6282  -15.4310   -1.6282   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0125
## I^2 (total heterogeneity / total variability):   97.09%
## H^2 (total variability / sampling variability):  34.37
## 
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0019  0.0065  -0.2928  0.7697  -0.0147  0.0109    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.9033   -5.8067   -1.8067   -3.6095   10.1933   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value):      0.0900
## I^2 (total heterogeneity / total variability):   97.85%
## H^2 (total variability / sampling variability):  46.60
## 
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2864  0.0458  -6.2497  <.0001  -0.3762  -0.1966  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.2. Driving risk-taking

Intercept only model

Meta analysis
ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.5193   -5.0386   -1.0386   -3.6523   10.9614   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0047 (SE = 0.0047)
## tau (square root of estimated tau^2 value):      0.0683
## I^2 (total heterogeneity / total variability):   98.86%
## H^2 (total variability / sampling variability):  87.97
## 
## Test for Heterogeneity:
## Q(df = 2) = 230.9570, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4651  0.0397  11.7072  <.0001  0.3872  0.5430  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   0.9190   -1.8381    4.1619   -1.8381   28.1619   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0093 (SE = 0.0132)
## tau (square root of estimated tau^2 value):             0.0963
## I^2 (residual heterogeneity / unaccounted variability): 99.55%
## H^2 (unaccounted variability / sampling variability):   222.84
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 222.8377, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0072, p-val = 0.9326
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt            0.4717  0.0969   4.8681  <.0001   0.2818  0.6616  *** 
## continentEurope   -0.0100  0.1185  -0.0846  0.9326  -0.2423  0.2223      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   0.9234   -1.8468    4.1532   -1.8468   28.1532   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0092 (SE = 0.0131)
## tau (square root of estimated tau^2 value):             0.0958
## I^2 (residual heterogeneity / unaccounted variability): 99.46%
## H^2 (unaccounted variability / sampling variability):   184.13
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 184.1325, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0169, p-val = 0.8966
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.5177  0.4093   1.2650  0.2059  -0.2844  1.3199    
## mean.age   -0.0009  0.0072  -0.1300  0.8966  -0.0151  0.0132    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.7744   -3.5487    2.4513   -3.5487   26.4513   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0016 (SE = 0.0024)
## tau (square root of estimated tau^2 value):             0.0402
## I^2 (residual heterogeneity / unaccounted variability): 96.14%
## H^2 (unaccounted variability / sampling variability):   25.91
## R^2 (amount of heterogeneity accounted for):            65.33%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 25.9141, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6424, p-val = 0.0312
## 
## Model Results:
## 
##          estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt    0.2037  0.1238  1.6447  0.1000  -0.0390  0.4463    
## scale1     0.0271  0.0126  2.1546  0.0312   0.0024  0.0517  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.6356   -9.2713   -5.2713   -7.8850    6.7287   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0229
## I^2 (total heterogeneity / total variability):   90.47%
## H^2 (total variability / sampling variability):  10.50
## 
## Test for Heterogeneity:
## Q(df = 2) = 28.1947, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1000  0.0141  -7.0814  <.0001  -0.1277  -0.0723  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1103   -4.2205    1.7795   -4.2205   25.7795   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value):             0.0287
## I^2 (residual heterogeneity / unaccounted variability): 95.70%
## H^2 (unaccounted variability / sampling variability):   23.24
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 23.2427, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2290, p-val = 0.6323
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.0869  0.0315  -2.7587  0.0058  -0.1487  -0.0252  ** 
## continentEurope   -0.0180  0.0377  -0.4785  0.6323  -0.0920   0.0559     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.2447   -4.4894    1.5106   -4.4894   25.5106   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value):             0.0247
## I^2 (residual heterogeneity / unaccounted variability): 92.96%
## H^2 (unaccounted variability / sampling variability):   14.20
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.2012, p-val = 0.0002
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6280, p-val = 0.4281
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1887  0.1132  -1.6673  0.0954  -0.4105  0.0331  . 
## mean.age    0.0016  0.0020   0.7925  0.4281  -0.0024  0.0055    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1912   -4.3825    1.6175   -4.3825   25.6175   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0006 (SE = 0.0010)
## tau (square root of estimated tau^2 value):             0.0253
## I^2 (residual heterogeneity / unaccounted variability): 87.39%
## H^2 (unaccounted variability / sampling variability):   7.93
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.9286, p-val = 0.0049
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5605, p-val = 0.4540
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0410  0.0799  -0.5134  0.6077  -0.1977  0.1156    
## scale1    -0.0061  0.0081  -0.7487  0.4540  -0.0220  0.0099    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.4849   -8.9698   -4.9698   -7.5835    7.0302   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0249
## I^2 (total heterogeneity / total variability):   91.76%
## H^2 (total variability / sampling variability):  12.13
## 
## Test for Heterogeneity:
## Q(df = 2) = 33.4609, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1003  0.0152  -6.5945  <.0001  -0.1300  -0.0705  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.0044   -4.0088    1.9912   -4.0088   25.9912   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0010 (SE = 0.0015)
## tau (square root of estimated tau^2 value):             0.0320
## I^2 (residual heterogeneity / unaccounted variability): 96.45%
## H^2 (unaccounted variability / sampling variability):   28.18
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 28.1843, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1685, p-val = 0.6814
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.0880  0.0345  -2.5500  0.0108  -0.1557  -0.0204  * 
## continentEurope   -0.0170  0.0415  -0.4105  0.6814  -0.0984   0.0643    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1213   -4.2425    1.7575   -4.2425   25.7575   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value):             0.0282
## I^2 (residual heterogeneity / unaccounted variability): 94.41%
## H^2 (unaccounted variability / sampling variability):   17.88
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.8824, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4918, p-val = 0.4831
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1882  0.1269  -1.4831  0.1380  -0.4368  0.0605    
## mean.age    0.0016  0.0023   0.7013  0.4831  -0.0028  0.0060    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1568   -4.3136    1.6864   -4.3136   25.6864   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value):             0.0263
## I^2 (residual heterogeneity / unaccounted variability): 88.47%
## H^2 (unaccounted variability / sampling variability):   8.67
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6732, p-val = 0.0032
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6974, p-val = 0.4037
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0321  0.0830  -0.3867  0.6990  -0.1948  0.1306    
## scale1    -0.0071  0.0084  -0.8351  0.4037  -0.0236  0.0095    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.4676   -8.9352   -4.9352   -7.5489    7.0648   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0251
## I^2 (total heterogeneity / total variability):   92.27%
## H^2 (total variability / sampling variability):  12.94
## 
## Test for Heterogeneity:
## Q(df = 2) = 35.9401, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1074  0.0153  -7.0185  <.0001  -0.1374  -0.0774  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.7846   -5.5692   -1.5692   -4.1829   10.4308   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value):      0.0560
## I^2 (total heterogeneity / total variability):   88.84%
## H^2 (total variability / sampling variability):  8.96
## 
## Test for Heterogeneity:
## Q(df = 2) = 16.3845, p-val = 0.0003
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3937  0.0344  -11.4325  <.0001  -0.4612  -0.3262  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.9907   -3.9814    2.0186   -3.9814   26.0186   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0011 (SE = 0.0015)
## tau (square root of estimated tau^2 value):             0.0325
## I^2 (residual heterogeneity / unaccounted variability): 96.69%
## H^2 (unaccounted variability / sampling variability):   30.22
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 30.2239, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1623, p-val = 0.6871
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.0953  0.0348  -2.7384  0.0062  -0.1636  -0.0271  ** 
## continentEurope   -0.0169  0.0419  -0.4028  0.6871  -0.0991   0.0653     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.1383   -2.2766    3.7234   -2.2766   27.7234   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0056 (SE = 0.0085)
## tau (square root of estimated tau^2 value):             0.0751
## I^2 (residual heterogeneity / unaccounted variability): 93.79%
## H^2 (unaccounted variability / sampling variability):   16.11
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.1108, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1979, p-val = 0.6564
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.4218  0.0787  -5.3571  <.0001  -0.5761  -0.2675  *** 
## continentEurope    0.0427  0.0959   0.4449  0.6564  -0.1453   0.2307      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1055   -4.2109    1.7891   -4.2109   25.7891   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value):             0.0287
## I^2 (residual heterogeneity / unaccounted variability): 94.81%
## H^2 (unaccounted variability / sampling variability):   19.28
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 19.2772, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4767, p-val = 0.4899
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1951  0.1285  -1.5180  0.1290  -0.4470  0.0568    
## mean.age    0.0016  0.0023   0.6905  0.4899  -0.0029  0.0060    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.0644   -2.1288    3.8712   -2.1288   27.8712   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0065 (SE = 0.0099)
## tau (square root of estimated tau^2 value):             0.0809
## I^2 (residual heterogeneity / unaccounted variability): 93.85%
## H^2 (unaccounted variability / sampling variability):   16.25
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.2482, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0441, p-val = 0.8337
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.3192  0.3545  -0.9003  0.3679  -1.0139  0.3756    
## mean.age   -0.0013  0.0062  -0.2100  0.8337  -0.0136  0.0109    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1519   -4.3038    1.6962   -4.3038   25.6962   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value):             0.0266
## I^2 (residual heterogeneity / unaccounted variability): 89.30%
## H^2 (unaccounted variability / sampling variability):   9.35
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.3483, p-val = 0.0022
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7082, p-val = 0.4001
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0383  0.0836  -0.4576  0.6472  -0.2021  0.1256    
## scale1    -0.0072  0.0085  -0.8415  0.4001  -0.0238  0.0095    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.9697   -5.9394    0.0606   -5.9394   24.0606   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0005)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1442, p-val = 0.7041
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 16.2403, p-val < .0001
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1346  0.0692  -1.9432  0.0520  -0.2703   0.0012    . 
## scale1    -0.0268  0.0067  -4.0299  <.0001  -0.0399  -0.0138  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender interaction model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   5.0033  -10.0066   -6.0066   -8.6203    5.9934   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0160
## I^2 (total heterogeneity / total variability):   70.46%
## H^2 (total variability / sampling variability):  3.39
## 
## Test for Heterogeneity:
## Q(df = 2) = 7.1299, p-val = 0.0283
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1250  0.0113  -11.0163  <.0001  -0.1472  -0.1027  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.8701   -5.7402   -1.7402   -4.3539   10.2598   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value):      0.0491
## I^2 (total heterogeneity / total variability):   78.69%
## H^2 (total variability / sampling variability):  4.69
## 
## Test for Heterogeneity:
## Q(df = 2) = 10.3844, p-val = 0.0056
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.4037  0.0334  -12.0808  <.0001  -0.4691  -0.3382  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.2941   -8.5881   -4.5881   -7.2018    7.4119   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value):      0.0239
## I^2 (total heterogeneity / total variability):   72.71%
## H^2 (total variability / sampling variability):  3.66
## 
## Test for Heterogeneity:
## Q(df = 2) = 7.9259, p-val = 0.0190
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0284  0.0165  1.7193  0.0856  -0.0040  0.0608  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.6212   -5.2424    0.7576   -5.2424   24.7576   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value):             0.0155
## I^2 (residual heterogeneity / unaccounted variability): 77.83%
## H^2 (unaccounted variability / sampling variability):   4.51
## R^2 (amount of heterogeneity accounted for):            6.21%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.5101, p-val = 0.0337
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9489, p-val = 0.3300
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1030  0.0253  -4.0791  <.0001  -0.1525  -0.0535  *** 
## continentEurope   -0.0274  0.0281  -0.9741  0.3300  -0.0825   0.0277      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.3515   -2.7030    3.2970   -2.7030   27.2970   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0035 (SE = 0.0055)
## tau (square root of estimated tau^2 value):             0.0594
## I^2 (residual heterogeneity / unaccounted variability): 90.07%
## H^2 (unaccounted variability / sampling variability):   10.07
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.0712, p-val = 0.0015
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3538, p-val = 0.5520
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.4474  0.0823  -5.4365  <.0001  -0.6087  -0.2861  *** 
## continentEurope    0.0556  0.0934   0.5948  0.5520  -0.1275   0.2387      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0010 (SE = 0.0016)
## tau (square root of estimated tau^2 value):             0.0314
## I^2 (residual heterogeneity / unaccounted variability): 87.21%
## H^2 (unaccounted variability / sampling variability):   7.82
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.8207, p-val = 0.0052
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2075, p-val = 0.6487
## 
## Model Results:
## 
##                  estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt            0.0127  0.0404  0.3130  0.7543  -0.0666  0.0919    
## continentEurope    0.0213  0.0469  0.4555  0.6487  -0.0705  0.1132    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.9434   -5.8867    0.1133   -5.8867   24.1133   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0086
## I^2 (residual heterogeneity / unaccounted variability): 45.74%
## H^2 (unaccounted variability / sampling variability):   1.84
## R^2 (amount of heterogeneity accounted for):            71.06%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8428, p-val = 0.1746
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7629, p-val = 0.0965
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.2445  0.0701  -3.4902  0.0005  -0.3818  -0.1072  *** 
## mean.age    0.0022  0.0013   1.6622  0.0965  -0.0004   0.0048    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.2226   -2.4451    3.5549   -2.4451   27.5549   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0045 (SE = 0.0072)
## tau (square root of estimated tau^2 value):             0.0674
## I^2 (residual heterogeneity / unaccounted variability): 89.59%
## H^2 (unaccounted variability / sampling variability):   9.61
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.6083, p-val = 0.0019
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1012, p-val = 0.7504
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.2985  0.3369  -0.8860  0.3756  -0.9589  0.3619    
## mean.age   -0.0019  0.0061  -0.3182  0.7504  -0.0139  0.0100    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0012 (SE = 0.0019)
## tau (square root of estimated tau^2 value):             0.0343
## I^2 (residual heterogeneity / unaccounted variability): 86.69%
## H^2 (unaccounted variability / sampling variability):   7.51
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.5112, p-val = 0.0061
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0422, p-val = 0.8372
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.0626  0.1671   0.3747  0.7079  -0.2649  0.3901    
## mean.age   -0.0006  0.0030  -0.2055  0.8372  -0.0065  0.0053    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1930   -4.3861    1.6139   -4.3861   25.6139   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0005 (SE = 0.0010)
## tau (square root of estimated tau^2 value):             0.0227
## I^2 (residual heterogeneity / unaccounted variability): 70.85%
## H^2 (unaccounted variability / sampling variability):   3.43
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.4308, p-val = 0.0640
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1168, p-val = 0.7325
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0977  0.0760  -1.2858  0.1985  -0.2467  0.0512    
## scale1    -0.0027  0.0078  -0.3418  0.7325  -0.0179  0.0126    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.2410   -4.4819    1.5181   -4.4819   25.5181   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0024)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0561, p-val = 0.8127
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 10.3282, p-val = 0.0013
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1888  0.0716  -2.6370  0.0084  -0.3292  -0.0485  ** 
## scale1    -0.0223  0.0069  -3.2138  0.0013  -0.0359  -0.0087  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.0544   -6.1088   -0.1088   -6.1088   23.8912   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0005)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0004, p-val = 0.9842
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.9255, p-val = 0.0049
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    0.1427  0.0438   3.2562  0.0011   0.0568   0.2286  ** 
## scale1    -0.0119  0.0042  -2.8152  0.0049  -0.0201  -0.0036  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3. Financial risk-taking

Intercept only model

Meta analysis
ICC’s results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  14.3821  -28.7643  -24.7643  -22.8754  -24.0143   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0042)
## tau (square root of estimated tau^2 value):      0.1105
## I^2 (total heterogeneity / total variability):   99.39%
## H^2 (total variability / sampling variability):  165.12
## 
## Test for Heterogeneity:
## Q(df = 19) = 2183.3189, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.3620  0.0253  14.3022  <.0001  0.3124  0.4116  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
ICC’s results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.9834  -23.9668  -13.9668  -10.1039   -7.9668   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0125 (SE = 0.0046)
## tau (square root of estimated tau^2 value):             0.1116
## I^2 (residual heterogeneity / unaccounted variability): 98.96%
## H^2 (unaccounted variability / sampling variability):   95.73
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 2113.1540, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.6844, p-val = 0.4429
## 
## Model Results:
## 
##                         estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt                   0.2601  0.1207  2.1562  0.0311   0.0237  0.4966  * 
## continentEurope           0.0939  0.1238  0.7584  0.4482  -0.1487  0.3365    
## continentNorth America    0.2033  0.1647  1.2340  0.2172  -0.1196  0.5261    
## continentOceania          0.2174  0.1644  1.3225  0.1860  -0.1048  0.5395    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
ICC’s results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  17.5947  -35.1894  -29.1894  -26.5182  -27.4751   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0077 (SE = 0.0028)
## tau (square root of estimated tau^2 value):             0.0879
## I^2 (residual heterogeneity / unaccounted variability): 99.00%
## H^2 (unaccounted variability / sampling variability):   100.32
## R^2 (amount of heterogeneity accounted for):            36.79%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 1793.1801, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 11.4540, p-val = 0.0007
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt     1.0059  0.1909   5.2705  <.0001   0.6318   1.3800  *** 
## mean.age   -0.0104  0.0031  -3.3844  0.0007  -0.0165  -0.0044  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
ICC’s results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  14.7012  -29.4025  -23.4025  -20.7314  -21.6882   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0108 (SE = 0.0038)
## tau (square root of estimated tau^2 value):             0.1038
## I^2 (residual heterogeneity / unaccounted variability): 99.06%
## H^2 (unaccounted variability / sampling variability):   106.46
## R^2 (amount of heterogeneity accounted for):            11.73%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 2154.3410, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.4514, p-val = 0.0632
## 
## Model Results:
## 
##          estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt    0.2622  0.0590  4.4439  <.0001   0.1465  0.3778  *** 
## scale2     0.0199  0.0107  1.8578  0.0632  -0.0011  0.0408    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  30.9609  -61.9217  -57.9217  -56.0328  -57.1717   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0436
## I^2 (total heterogeneity / total variability):   97.03%
## H^2 (total variability / sampling variability):  33.68
## 
## Test for Heterogeneity:
## Q(df = 19) = 398.5644, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1128  0.0104  -10.8620  <.0001  -0.1332  -0.0925  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  25.6893  -51.3787  -41.3787  -37.5157  -35.3787   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0446
## I^2 (residual heterogeneity / unaccounted variability): 95.69%
## H^2 (unaccounted variability / sampling variability):   23.22
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 176.0198, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0780, p-val = 0.5564
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.1377  0.0559  -2.4640  0.0137  -0.2473  -0.0282  * 
## continentEurope           0.0221  0.0571   0.3869  0.6989  -0.0898   0.1339    
## continentNorth America    0.0218  0.0728   0.2999  0.7643  -0.1209   0.1646    
## continentOceania          0.0851  0.0716   1.1890  0.2345  -0.0552   0.2254    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  31.5690  -63.1380  -57.1380  -54.4669  -55.4237   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0014 (SE = 0.0005)
## tau (square root of estimated tau^2 value):             0.0369
## I^2 (residual heterogeneity / unaccounted variability): 95.52%
## H^2 (unaccounted variability / sampling variability):   22.32
## R^2 (amount of heterogeneity accounted for):            28.70%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 201.3723, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.7292, p-val = 0.0095
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt     0.0981  0.0815   1.2031  0.2289  -0.0617   0.2578     
## mean.age   -0.0034  0.0013  -2.5941  0.0095  -0.0060  -0.0008  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  29.2781  -58.5563  -52.5563  -49.8852  -50.8420   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0438
## I^2 (residual heterogeneity / unaccounted variability): 95.98%
## H^2 (unaccounted variability / sampling variability):   24.88
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 362.4088, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9895, p-val = 0.3199
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1362  0.0257  -5.3019  <.0001  -0.1866  -0.0859  *** 
## scale2     0.0046  0.0046   0.9948  0.3199  -0.0045   0.0136      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  30.6562  -61.3124  -57.3124  -55.4235  -56.5624   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0447
## I^2 (total heterogeneity / total variability):   96.85%
## H^2 (total variability / sampling variability):  31.72
## 
## Test for Heterogeneity:
## Q(df = 19) = 370.8055, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1130  0.0106  -10.6696  <.0001  -0.1337  -0.0922  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  25.3990  -50.7980  -40.7980  -36.9351  -34.7980   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0021 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0459
## I^2 (residual heterogeneity / unaccounted variability): 95.76%
## H^2 (unaccounted variability / sampling variability):   23.56
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 187.4807, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.9953, p-val = 0.5734
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.1419  0.0562  -2.5262  0.0115  -0.2520  -0.0318  * 
## continentEurope           0.0265  0.0574   0.4612  0.6447  -0.0860   0.1390    
## continentNorth America    0.0255  0.0738   0.3450  0.7301  -0.1192   0.1701    
## continentOceania          0.0885  0.0726   1.2193  0.2227  -0.0538   0.2307    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  30.8793  -61.7586  -55.7586  -53.0875  -54.0443   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0015 (SE = 0.0006)
## tau (square root of estimated tau^2 value):             0.0392
## I^2 (residual heterogeneity / unaccounted variability): 95.59%
## H^2 (unaccounted variability / sampling variability):   22.67
## R^2 (amount of heterogeneity accounted for):            23.20%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 212.5169, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.5323, p-val = 0.0187
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt     0.0895  0.0864   1.0362  0.3001  -0.0798   0.2588    
## mean.age   -0.0033  0.0014  -2.3521  0.0187  -0.0060  -0.0005  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  28.9455  -57.8910  -51.8910  -49.2199  -50.1767   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0450
## I^2 (residual heterogeneity / unaccounted variability): 95.88%
## H^2 (unaccounted variability / sampling variability):   24.24
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 349.9235, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8848, p-val = 0.3469
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1356  0.0263  -5.1594  <.0001  -0.1871  -0.0841  *** 
## scale2     0.0045  0.0047   0.9407  0.3469  -0.0048   0.0137      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  31.3718  -62.7436  -58.7436  -56.8547  -57.9936   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0429
## I^2 (total heterogeneity / total variability):   96.68%
## H^2 (total variability / sampling variability):  30.09
## 
## Test for Heterogeneity:
## Q(df = 19) = 368.9261, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1139  0.0102  -11.1850  <.0001  -0.1339  -0.0940  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  14.5166  -29.0332  -25.0332  -23.1444  -24.2832   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0042)
## tau (square root of estimated tau^2 value):      0.1093
## I^2 (total heterogeneity / total variability):   96.56%
## H^2 (total variability / sampling variability):  29.05
## 
## Test for Heterogeneity:
## Q(df = 19) = 1010.6427, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.2717  0.0254  -10.6780  <.0001  -0.3216  -0.2218  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  26.1499  -52.2997  -42.2997  -38.4368  -36.2997   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0436
## I^2 (residual heterogeneity / unaccounted variability): 95.45%
## H^2 (unaccounted variability / sampling variability):   21.98
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 170.5647, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.3348, p-val = 0.5059
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.1474  0.0540  -2.7280  0.0064  -0.2533  -0.0415  ** 
## continentEurope           0.0315  0.0552   0.5714  0.5677  -0.0766   0.1397     
## continentNorth America    0.0236  0.0707   0.3340  0.7384  -0.1149   0.1621     
## continentOceania          0.0932  0.0694   1.3418  0.1797  -0.0429   0.2293     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  13.1153  -26.2307  -16.2307  -12.3677  -10.2307   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0105 (SE = 0.0040)
## tau (square root of estimated tau^2 value):             0.1024
## I^2 (residual heterogeneity / unaccounted variability): 95.06%
## H^2 (unaccounted variability / sampling variability):   20.25
## R^2 (amount of heterogeneity accounted for):            12.27%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 480.3704, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.1956, p-val = 0.1580
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.2941  0.1160  -2.5358  0.0112  -0.5214  -0.0668  * 
## continentEurope           0.0186  0.1188   0.1567  0.8755  -0.2143   0.2516    
## continentNorth America   -0.1007  0.1568  -0.6425  0.5205  -0.4080   0.2065    
## continentOceania          0.2219  0.1549   1.4325  0.1520  -0.0817   0.5255    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  31.8571  -63.7141  -57.7141  -55.0430  -55.9999   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0014 (SE = 0.0005)
## tau (square root of estimated tau^2 value):             0.0369
## I^2 (residual heterogeneity / unaccounted variability): 95.21%
## H^2 (unaccounted variability / sampling variability):   20.88
## R^2 (amount of heterogeneity accounted for):            25.83%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 219.6926, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.3061, p-val = 0.0120
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt     0.0906  0.0817   1.1091  0.2674  -0.0695   0.2506    
## mean.age   -0.0033  0.0013  -2.5112  0.0120  -0.0059  -0.0007  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  13.4274  -26.8548  -20.8548  -18.1836  -19.1405   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0124 (SE = 0.0045)
## tau (square root of estimated tau^2 value):             0.1113
## I^2 (residual heterogeneity / unaccounted variability): 96.23%
## H^2 (unaccounted variability / sampling variability):   26.51
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 1003.3146, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3335, p-val = 0.5636
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.4117  0.2437  -1.6896  0.0911  -0.8892  0.0659  . 
## mean.age    0.0023  0.0039   0.5775  0.5636  -0.0054  0.0100    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  29.4892  -58.9783  -52.9783  -50.3072  -51.2640   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0436
## I^2 (residual heterogeneity / unaccounted variability): 95.72%
## H^2 (unaccounted variability / sampling variability):   23.38
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 342.9145, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6091, p-val = 0.4351
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1322  0.0255  -5.1854  <.0001  -0.1821  -0.0822  *** 
## scale2     0.0036  0.0046   0.7805  0.4351  -0.0054   0.0126      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  15.7343  -31.4686  -25.4686  -22.7975  -23.7543   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0091 (SE = 0.0034)
## tau (square root of estimated tau^2 value):             0.0956
## I^2 (residual heterogeneity / unaccounted variability): 95.08%
## H^2 (unaccounted variability / sampling variability):   20.34
## R^2 (amount of heterogeneity accounted for):            23.59%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 473.9443, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.8991, p-val = 0.0151
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1477  0.0555  -2.6618  0.0078  -0.2565  -0.0390  ** 
## scale2    -0.0243  0.0100  -2.4288  0.0151  -0.0440  -0.0047   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender interaction model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  27.0899  -54.1797  -50.1797  -48.2909  -49.4297   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0027 (SE = 0.0010)
## tau (square root of estimated tau^2 value):      0.0523
## I^2 (total heterogeneity / total variability):   95.50%
## H^2 (total variability / sampling variability):  22.22
## 
## Test for Heterogeneity:
## Q(df = 19) = 276.9773, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1317  0.0127  -10.3514  <.0001  -0.1566  -0.1068  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  10.6323  -21.2646  -17.2646  -15.3757  -16.5146   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0152 (SE = 0.0060)
## tau (square root of estimated tau^2 value):      0.1234
## I^2 (total heterogeneity / total variability):   95.06%
## H^2 (total variability / sampling variability):  20.23
## 
## Test for Heterogeneity:
## Q(df = 19) = 874.0879, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3173  0.0306  -10.3622  <.0001  -0.3773  -0.2572  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  34.0688  -68.1376  -64.1376  -62.2488  -63.3876   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0256
## I^2 (total heterogeneity / total variability):   73.04%
## H^2 (total variability / sampling variability):  3.71
## 
## Test for Heterogeneity:
## Q(df = 19) = 56.0213, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.0309  0.0080  3.8545  0.0001  0.0152  0.0467  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  22.8412  -45.6825  -35.6825  -31.8195  -29.6825   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0027 (SE = 0.0011)
## tau (square root of estimated tau^2 value):             0.0520
## I^2 (residual heterogeneity / unaccounted variability): 93.34%
## H^2 (unaccounted variability / sampling variability):   15.02
## R^2 (amount of heterogeneity accounted for):            1.02%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 111.7893, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.9769, p-val = 0.3952
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.1796  0.0701  -2.5603  0.0105  -0.3170  -0.0421  * 
## continentEurope           0.0427  0.0715   0.5974  0.5502  -0.0974   0.1828    
## continentNorth America    0.0805  0.0899   0.8957  0.3704  -0.0957   0.2568    
## continentOceania          0.1222  0.0874   1.3978  0.1622  -0.0491   0.2935    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.9661  -19.9323   -9.9323   -6.0693   -3.9323   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0123 (SE = 0.0054)
## tau (square root of estimated tau^2 value):             0.1109
## I^2 (residual heterogeneity / unaccounted variability): 90.91%
## H^2 (unaccounted variability / sampling variability):   11.01
## R^2 (amount of heterogeneity accounted for):            19.25%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 118.4667, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.4533, p-val = 0.1415
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.4145  0.1792  -2.3134  0.0207  -0.7658  -0.0633  * 
## continentEurope           0.0834  0.1817   0.4590  0.6462  -0.2727   0.4396    
## continentNorth America    0.0997  0.2190   0.4552  0.6489  -0.3295   0.5288    
## continentOceania          0.3445  0.2108   1.6339  0.1023  -0.0688   0.7578    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value):             0.0207
## I^2 (residual heterogeneity / unaccounted variability): 53.86%
## H^2 (unaccounted variability / sampling variability):   2.17
## R^2 (amount of heterogeneity accounted for):            34.53%
## 
## Test for Residual Heterogeneity:
## QE(df = 16) = 32.7135, p-val = 0.0081
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.4100, p-val = 0.0933
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                   0.0595  0.0674   0.8833  0.3770  -0.0725  0.1916    
## continentEurope          -0.0228  0.0678  -0.3365  0.7365  -0.1558  0.1101    
## continentNorth America   -0.1000  0.0756  -1.3235  0.1857  -0.2482  0.0481    
## continentOceania         -0.0530  0.0706  -0.7501  0.4532  -0.1914  0.0854    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  27.6317  -55.2634  -49.2634  -46.5923  -47.5491   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0451
## I^2 (residual heterogeneity / unaccounted variability): 93.59%
## H^2 (unaccounted variability / sampling variability):   15.61
## R^2 (amount of heterogeneity accounted for):            25.70%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 155.7586, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.9073, p-val = 0.0151
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt     0.1131  0.1009   1.1208  0.2624  -0.0846   0.3107    
## mean.age   -0.0040  0.0016  -2.4305  0.0151  -0.0072  -0.0008  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.5990  -19.1980  -13.1980  -10.5269  -11.4837   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0163 (SE = 0.0066)
## tau (square root of estimated tau^2 value):             0.1276
## I^2 (residual heterogeneity / unaccounted variability): 95.06%
## H^2 (unaccounted variability / sampling variability):   20.23
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 810.0881, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1349, p-val = 0.7134
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.2137  0.2850  -0.7496  0.4535  -0.7723  0.3450    
## mean.age   -0.0017  0.0046  -0.3673  0.7134  -0.0108  0.0074    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value):             0.0267
## I^2 (residual heterogeneity / unaccounted variability): 72.87%
## H^2 (unaccounted variability / sampling variability):   3.69
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 46.3798, p-val = 0.0003
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6195, p-val = 0.4312
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0214  0.0672  -0.3185  0.7501  -0.1530  0.1102    
## mean.age    0.0009  0.0011   0.7871  0.4312  -0.0013  0.0031    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  25.8677  -51.7355  -45.7355  -43.0644  -44.0212   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0027 (SE = 0.0011)
## tau (square root of estimated tau^2 value):             0.0518
## I^2 (residual heterogeneity / unaccounted variability): 93.75%
## H^2 (unaccounted variability / sampling variability):   16.00
## R^2 (amount of heterogeneity accounted for):            1.87%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 253.3383, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5424, p-val = 0.2143
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1669  0.0311  -5.3714  <.0001  -0.2278  -0.1060  *** 
## scale2     0.0069  0.0056   1.2419  0.2143  -0.0040   0.0178      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.8119  -19.6239  -13.6239  -10.9528  -11.9096   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0153 (SE = 0.0062)
## tau (square root of estimated tau^2 value):             0.1239
## I^2 (residual heterogeneity / unaccounted variability): 92.97%
## H^2 (unaccounted variability / sampling variability):   14.22
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 383.0457, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5759, p-val = 0.4479
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.2649  0.0756  -3.5055  0.0005  -0.4130  -0.1168  *** 
## scale2    -0.0102  0.0134  -0.7589  0.4479  -0.0366   0.0161      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  32.2041  -64.4082  -58.4082  -55.7370  -56.6939   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value):             0.0272
## I^2 (residual heterogeneity / unaccounted variability): 68.88%
## H^2 (unaccounted variability / sampling variability):   3.21
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 55.1806, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2701, p-val = 0.2597
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    0.0519  0.0202   2.5665  0.0103   0.0123  0.0915  * 
## scale2    -0.0038  0.0034  -1.1270  0.2597  -0.0105  0.0028    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Quadratic model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  12.3000  -24.6001  -20.6001  -19.6303  -19.2668   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0036 (SE = 0.0022)
## tau (square root of estimated tau^2 value):      0.0597
## I^2 (total heterogeneity / total variability):   96.50%
## H^2 (total variability / sampling variability):  28.59
## 
## Test for Heterogeneity:
## Q(df = 12) = 75.0873, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1042  0.0208  -5.0134  <.0001  -0.1449  -0.0635  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  29.6130  -59.2261  -55.2261  -54.2562  -53.8927   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value):      0.0167
## I^2 (total heterogeneity / total variability):   91.39%
## H^2 (total variability / sampling variability):  11.61
## 
## Test for Heterogeneity:
## Q(df = 12) = 269.2438, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0012  0.0057  0.2132  0.8312  -0.0100  0.0125    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  10.7514  -21.5027  -15.5027  -14.3091  -12.0742   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0042 (SE = 0.0027)
## tau (square root of estimated tau^2 value):             0.0648
## I^2 (residual heterogeneity / unaccounted variability): 91.65%
## H^2 (unaccounted variability / sampling variability):   11.97
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 50.7173, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5397, p-val = 0.4625
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0605  0.0648  -0.9337  0.3505  -0.1876  0.0665    
## continentEurope   -0.0506  0.0689  -0.7347  0.4625  -0.1858  0.0845    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  27.5310  -55.0620  -49.0620  -47.8683  -45.6334   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0145
## I^2 (residual heterogeneity / unaccounted variability): 75.57%
## H^2 (unaccounted variability / sampling variability):   4.09
## R^2 (amount of heterogeneity accounted for):            24.16%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 31.1098, p-val = 0.0011
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.6416, p-val = 0.1041
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0209  0.0146  -1.4334  0.1517  -0.0495  0.0077    
## continentEurope    0.0254  0.0156   1.6253  0.1041  -0.0052  0.0560    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.4073  -22.8146  -16.8146  -15.6209  -13.3860   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0031 (SE = 0.0021)
## tau (square root of estimated tau^2 value):             0.0556
## I^2 (residual heterogeneity / unaccounted variability): 95.14%
## H^2 (unaccounted variability / sampling variability):   20.56
## R^2 (amount of heterogeneity accounted for):            13.47%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 59.2910, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9591, p-val = 0.1616
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.1336  0.1703   0.7844  0.4328  -0.2002  0.4674    
## mean.age   -0.0040  0.0028  -1.3997  0.1616  -0.0095  0.0016    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  26.5755  -53.1511  -47.1511  -45.9574  -43.7225   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0179
## I^2 (residual heterogeneity / unaccounted variability): 91.31%
## H^2 (unaccounted variability / sampling variability):   11.51
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 212.8375, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0201, p-val = 0.8873
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0064  0.0538  -0.1181  0.9060  -0.1118  0.0991    
## mean.age    0.0001  0.0009   0.1417  0.8873  -0.0016  0.0019    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  10.7959  -21.5918  -15.5918  -14.3982  -12.1633   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0042 (SE = 0.0027)
## tau (square root of estimated tau^2 value):             0.0647
## I^2 (residual heterogeneity / unaccounted variability): 92.05%
## H^2 (unaccounted variability / sampling variability):   12.57
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 58.0105, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5703, p-val = 0.4501
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1671  0.0848  -1.9712  0.0487  -0.3333  -0.0010  * 
## scale2     0.0132  0.0175   0.7552  0.4501  -0.0210   0.0474    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  27.5924  -55.1847  -49.1847  -47.9910  -45.7561   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value):             0.0144
## I^2 (residual heterogeneity / unaccounted variability): 77.79%
## H^2 (unaccounted variability / sampling variability):   4.50
## R^2 (amount of heterogeneity accounted for):            25.38%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 39.3616, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7049, p-val = 0.1000
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0301  0.0197  -1.5244  0.1274  -0.0687  0.0086    
## scale2     0.0066  0.0040   1.6447  0.1000  -0.0013  0.0145    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Quadratic with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  12.2897  -24.5794  -20.5794  -19.6096  -19.2461   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0040 (SE = 0.0024)
## tau (square root of estimated tau^2 value):      0.0629
## I^2 (total heterogeneity / total variability):   96.95%
## H^2 (total variability / sampling variability):  32.83
## 
## Test for Heterogeneity:
## Q(df = 12) = 108.8162, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1187  0.0215  -5.5164  <.0001  -0.1608  -0.0765  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  30.0344  -60.0689  -56.0689  -55.0990  -54.7355   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value):      0.0162
## I^2 (total heterogeneity / total variability):   91.16%
## H^2 (total variability / sampling variability):  11.31
## 
## Test for Heterogeneity:
## Q(df = 12) = 287.9791, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0045  0.0056  0.7999  0.4238  -0.0065  0.0155    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.1888  -18.3776  -14.3776  -13.4078  -13.0443   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0052)
## tau (square root of estimated tau^2 value):      0.1084
## I^2 (total heterogeneity / total variability):   96.26%
## H^2 (total variability / sampling variability):  26.77
## 
## Test for Heterogeneity:
## Q(df = 12) = 627.3178, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2681  0.0312  -8.5824  <.0001  -0.3294  -0.2069  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  10.9416  -21.8831  -15.8831  -14.6894  -12.4545   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0043 (SE = 0.0027)
## tau (square root of estimated tau^2 value):             0.0654
## I^2 (residual heterogeneity / unaccounted variability): 92.12%
## H^2 (unaccounted variability / sampling variability):   12.69
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 61.0752, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8908, p-val = 0.3453
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0612  0.0654  -0.9365  0.3490  -0.1894  0.0669    
## continentEurope   -0.0656  0.0695  -0.9438  0.3453  -0.2018  0.0706    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  28.4516  -56.9031  -50.9031  -49.7094  -47.4746   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value):             0.0124
## I^2 (residual heterogeneity / unaccounted variability): 69.92%
## H^2 (unaccounted variability / sampling variability):   3.32
## R^2 (amount of heterogeneity accounted for):            41.35%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 25.3747, p-val = 0.0080
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6370, p-val = 0.0313
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0208  0.0125  -1.6644  0.0960  -0.0452  0.0037  . 
## continentEurope    0.0290  0.0135   2.1534  0.0313   0.0026  0.0553  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.9064  -19.8127  -13.8127  -12.6190  -10.3841   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0086 (SE = 0.0041)
## tau (square root of estimated tau^2 value):             0.0927
## I^2 (residual heterogeneity / unaccounted variability): 92.63%
## H^2 (unaccounted variability / sampling variability):   13.58
## R^2 (amount of heterogeneity accounted for):            26.92%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 184.0555, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.8997, p-val = 0.0269
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.0700  0.0930  -0.7529  0.4515  -0.2523   0.1123    
## continentEurope   -0.2151  0.0972  -2.2135  0.0269  -0.4056  -0.0246  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  11.7695  -23.5390  -17.5390  -16.3453  -14.1104   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0030 (SE = 0.0021)
## tau (square root of estimated tau^2 value):             0.0548
## I^2 (residual heterogeneity / unaccounted variability): 95.20%
## H^2 (unaccounted variability / sampling variability):   20.82
## R^2 (amount of heterogeneity accounted for):            24.01%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 80.5192, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8400, p-val = 0.0919
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.1652  0.1681   0.9828  0.3257  -0.1642  0.4945    
## mean.age   -0.0047  0.0028  -1.6852  0.0919  -0.0102  0.0008  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  27.0700  -54.1399  -48.1399  -46.9462  -44.7114   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0171
## I^2 (residual heterogeneity / unaccounted variability): 90.82%
## H^2 (unaccounted variability / sampling variability):   10.89
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 216.8083, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2241, p-val = 0.6360
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0197  0.0516  -0.3820  0.7024  -0.1209  0.0814    
## mean.age    0.0004  0.0008   0.4733  0.6360  -0.0013  0.0021    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   7.9280  -15.8560   -9.8560   -8.6623   -6.4275   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0129 (SE = 0.0059)
## tau (square root of estimated tau^2 value):             0.1138
## I^2 (residual heterogeneity / unaccounted variability): 95.78%
## H^2 (unaccounted variability / sampling variability):   23.71
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 515.3742, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0007, p-val = 0.9787
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.2771  0.3255  -0.8514  0.3946  -0.9150  0.3608    
## mean.age    0.0001  0.0052   0.0267  0.9787  -0.0101  0.0104    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  10.9257  -21.8514  -15.8514  -14.6577  -12.4228   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0044 (SE = 0.0028)
## tau (square root of estimated tau^2 value):             0.0665
## I^2 (residual heterogeneity / unaccounted variability): 92.75%
## H^2 (unaccounted variability / sampling variability):   13.79
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 73.6877, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7831, p-val = 0.3762
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1937  0.0866  -2.2361  0.0253  -0.3634  -0.0239  * 
## scale2     0.0158  0.0179   0.8849  0.3762  -0.0192   0.0509    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age\({ }^{2}\) effect
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  27.6087  -55.2174  -49.2174  -48.0238  -45.7889   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0150
## I^2 (residual heterogeneity / unaccounted variability): 79.59%
## H^2 (unaccounted variability / sampling variability):   4.90
## R^2 (amount of heterogeneity accounted for):            14.35%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 47.3630, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.6255, p-val = 0.2023
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0206  0.0203  -1.0142  0.3105  -0.0604  0.0192    
## scale2     0.0053  0.0042   1.2750  0.2023  -0.0028  0.0135    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   9.1025  -18.2050  -12.2050  -11.0113   -8.7765   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0099 (SE = 0.0046)
## tau (square root of estimated tau^2 value):             0.0996
## I^2 (residual heterogeneity / unaccounted variability): 95.17%
## H^2 (unaccounted variability / sampling variability):   20.69
## R^2 (amount of heterogeneity accounted for):            15.60%
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 306.0790, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7118, p-val = 0.0996
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0744  0.1208  -0.6158  0.5380  -0.3111  0.1624    
## scale2    -0.0431  0.0262  -1.6468  0.0996  -0.0943  0.0082  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.4. Recreational risk-taking

Intercept only model

Meta analysis
ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.2391   -6.4783   -2.4783   -5.0920    9.5217   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0023 (SE = 0.0023)
## tau (square root of estimated tau^2 value):      0.0476
## I^2 (total heterogeneity / total variability):   98.16%
## H^2 (total variability / sampling variability):  54.25
## 
## Test for Heterogeneity:
## Q(df = 2) = 151.9451, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4688  0.0278  16.8697  <.0001  0.4143  0.5232  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.2832   -2.5664    3.4336   -2.5664   27.4336   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0045 (SE = 0.0064)
## tau (square root of estimated tau^2 value):             0.0668
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability):   138.41
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 138.4144, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0112, p-val = 0.9157
## 
## Model Results:
## 
##                  estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt            0.4628  0.0675  6.8542  <.0001   0.3304  0.5951  *** 
## continentEurope    0.0087  0.0825  0.1058  0.9157  -0.1530  0.1704      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.3288   -2.6576    3.3424   -2.6576   27.3424   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0041 (SE = 0.0058)
## tau (square root of estimated tau^2 value):             0.0638
## I^2 (residual heterogeneity / unaccounted variability): 99.05%
## H^2 (unaccounted variability / sampling variability):   105.13
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 105.1267, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1096, p-val = 0.7406
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.5582  0.2732   2.0435  0.0410   0.0228  1.0937  * 
## mean.age   -0.0016  0.0048  -0.3311  0.7406  -0.0110  0.0078    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.8111   -3.6222    2.3778   -3.6222   26.3778   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0015 (SE = 0.0022)
## tau (square root of estimated tau^2 value):             0.0389
## I^2 (residual heterogeneity / unaccounted variability): 96.61%
## H^2 (unaccounted variability / sampling variability):   29.47
## R^2 (amount of heterogeneity accounted for):            33.24%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 29.4684, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9478, p-val = 0.1628
## 
## Model Results:
## 
##          estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt    0.3056  0.1193  2.5625  0.0104   0.0719  0.5393  * 
## scale1     0.0169  0.0121  1.3956  0.1628  -0.0068  0.0406    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.5828   -7.1656   -3.1656   -5.7793    8.8344   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value):      0.0397
## I^2 (total heterogeneity / total variability):   96.50%
## H^2 (total variability / sampling variability):  28.59
## 
## Test for Heterogeneity:
## Q(df = 2) = 79.5694, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1674  0.0235  -7.1081  <.0001  -0.2135  -0.1212  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5967   -3.1933    2.8067   -3.1933   26.8067   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value):             0.0486
## I^2 (residual heterogeneity / unaccounted variability): 98.46%
## H^2 (unaccounted variability / sampling variability):   64.77
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 64.7722, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3148, p-val = 0.5747
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1436  0.0506  -2.8352  0.0046  -0.2428  -0.0443  ** 
## continentEurope   -0.0344  0.0614  -0.5611  0.5747  -0.1547   0.0858     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.7508   -3.5015    2.4985   -3.5015   26.4985   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value):             0.0414
## I^2 (residual heterogeneity / unaccounted variability): 97.31%
## H^2 (unaccounted variability / sampling variability):   37.18
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 37.1791, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7964, p-val = 0.3722
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.3282  0.1820  -1.8036  0.0713  -0.6849  0.0285  . 
## mean.age    0.0029  0.0032   0.8924  0.3722  -0.0034  0.0092    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6048   -3.2096    2.7904   -3.2096   26.7904   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0023 (SE = 0.0033)
## tau (square root of estimated tau^2 value):             0.0475
## I^2 (residual heterogeneity / unaccounted variability): 95.49%
## H^2 (unaccounted variability / sampling variability):   22.19
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 22.1883, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3765, p-val = 0.5395
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0795  0.1454  -0.5469  0.5845  -0.3645  0.2055    
## scale1    -0.0091  0.0148  -0.6136  0.5395  -0.0381  0.0199    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.4996   -6.9992   -2.9992   -5.6129    9.0008   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0018)
## tau (square root of estimated tau^2 value):      0.0415
## I^2 (total heterogeneity / total variability):   96.82%
## H^2 (total variability / sampling variability):  31.41
## 
## Test for Heterogeneity:
## Q(df = 2) = 91.5192, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1648  0.0246  -6.7105  <.0001  -0.2130  -0.1167  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5080   -3.0160    2.9840   -3.0160   26.9840   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value):             0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.72%
## H^2 (unaccounted variability / sampling variability):   78.00
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 77.9964, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1987, p-val = 0.6558
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1443  0.0550  -2.6214  0.0088  -0.2522  -0.0364  ** 
## continentEurope   -0.0298  0.0668  -0.4457  0.6558  -0.1607   0.1012     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6332   -3.2665    2.7335   -3.2665   26.7335   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value):             0.0468
## I^2 (residual heterogeneity / unaccounted variability): 97.89%
## H^2 (unaccounted variability / sampling variability):   47.43
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 47.4250, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5459, p-val = 0.4600
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.3139  0.2039  -1.5397  0.1236  -0.7136  0.0857    
## mean.age    0.0027  0.0036   0.7388  0.4600  -0.0044  0.0097    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6255   -3.2510    2.7490   -3.2510   26.7490   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value):             0.0465
## I^2 (residual heterogeneity / unaccounted variability): 95.33%
## H^2 (unaccounted variability / sampling variability):   21.43
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 21.4300, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5626, p-val = 0.4532
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0599  0.1424  -0.4206  0.6741  -0.3389  0.2192    
## scale1    -0.0109  0.0145  -0.7501  0.4532  -0.0392  0.0175    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.6536   -7.3073   -3.3073   -5.9210    8.6927   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value):      0.0384
## I^2 (total heterogeneity / total variability):   96.43%
## H^2 (total variability / sampling variability):  28.02
## 
## Test for Heterogeneity:
## Q(df = 2) = 82.9062, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1703  0.0228  -7.4715  <.0001  -0.2149  -0.1256  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.7562   -5.5124   -1.5124   -4.1261   10.4876   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value):      0.0556
## I^2 (total heterogeneity / total variability):   88.03%
## H^2 (total variability / sampling variability):  8.35
## 
## Test for Heterogeneity:
## Q(df = 2) = 12.5156, p-val = 0.0019
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3731  0.0344  -10.8491  <.0001  -0.4404  -0.3057  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5614   -3.1228    2.8772   -3.1228   26.8772   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value):             0.0504
## I^2 (residual heterogeneity / unaccounted variability): 98.61%
## H^2 (unaccounted variability / sampling variability):   72.15
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 72.1466, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1397, p-val = 0.7086
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1538  0.0522  -2.9450  0.0032  -0.2562  -0.0515  ** 
## continentEurope   -0.0237  0.0634  -0.3738  0.7086  -0.1479   0.1005     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1419   -4.2838    1.7162   -4.2838   25.7162   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0004 (SE = 0.0011)
## tau (square root of estimated tau^2 value):             0.0207
## I^2 (residual heterogeneity / unaccounted variability): 53.32%
## H^2 (unaccounted variability / sampling variability):   2.14
## R^2 (amount of heterogeneity accounted for):            86.06%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.1424, p-val = 0.1433
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.3646, p-val = 0.0116
## 
## Model Results:
## 
##                  estimate      se      zval    pval    ci.lb    ci.ub 
## intrcpt           -0.4421  0.0331  -13.3624  <.0001  -0.5070  -0.3773  *** 
## continentEurope    0.0967  0.0383    2.5228  0.0116   0.0216   0.1718    * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6692   -3.3384    2.6616   -3.3384   26.6616   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0020 (SE = 0.0029)
## tau (square root of estimated tau^2 value):             0.0451
## I^2 (residual heterogeneity / unaccounted variability): 97.81%
## H^2 (unaccounted variability / sampling variability):   45.58
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 45.5752, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4202, p-val = 0.5168
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.2964  0.1967  -1.5069  0.1318  -0.6818  0.0891    
## mean.age    0.0023  0.0035   0.6482  0.5168  -0.0046  0.0091    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6730   -3.3461    2.6539   -3.3461   26.6539   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0016 (SE = 0.0029)
## tau (square root of estimated tau^2 value):             0.0403
## I^2 (residual heterogeneity / unaccounted variability): 78.80%
## H^2 (unaccounted variability / sampling variability):   4.72
## R^2 (amount of heterogeneity accounted for):            47.39%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.7169, p-val = 0.0299
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.4489, p-val = 0.1176
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0721  0.1937  -0.3723  0.7096  -0.4517  0.3075    
## mean.age   -0.0054  0.0034  -1.5649  0.1176  -0.0121  0.0014    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.7531   -3.5062    2.4938   -3.5062   26.4938   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value):             0.0407
## I^2 (residual heterogeneity / unaccounted variability): 94.33%
## H^2 (unaccounted variability / sampling variability):   17.64
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.6441, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7341, p-val = 0.3915
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0650  0.1251  -0.5191  0.6037  -0.3102  0.1803    
## scale1    -0.0109  0.0127  -0.8568  0.3915  -0.0359  0.0140    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.4286   -2.8572    3.1428   -2.8572   27.1428   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0030 (SE = 0.0048)
## tau (square root of estimated tau^2 value):             0.0543
## I^2 (residual heterogeneity / unaccounted variability): 87.74%
## H^2 (unaccounted variability / sampling variability):   8.16
## R^2 (amount of heterogeneity accounted for):            4.49%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.1573, p-val = 0.0043
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1699, p-val = 0.2794
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.1832  0.1787  -1.0256  0.3051  -0.5334  0.1669    
## scale1    -0.0195  0.0181  -1.0816  0.2794  -0.0549  0.0159    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender interaction model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.3138   -6.6277   -2.6277   -5.2414    9.3723   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0022)
## tau (square root of estimated tau^2 value):      0.0443
## I^2 (total heterogeneity / total variability):   94.60%
## H^2 (total variability / sampling variability):  18.51
## 
## Test for Heterogeneity:
## Q(df = 2) = 42.6115, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1838  0.0268  -6.8594  <.0001  -0.2363  -0.1313  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.2999   -8.5998   -4.5998   -7.2135    7.4002   
## 
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 2) = 1.5405, p-val = 0.4629
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3629  0.0111  -32.5617  <.0001  -0.3847  -0.3410  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   4.0570   -8.1139   -4.1139   -6.7276    7.8861   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0190
## I^2 (total heterogeneity / total variability):   62.05%
## H^2 (total variability / sampling variability):  2.64
## 
## Test for Heterogeneity:
## Q(df = 2) = 4.6848, p-val = 0.0961
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.0331  0.0142  2.3373  0.0194  0.0053  0.0608  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6478   -3.2956    2.7044   -3.2956   26.7044   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value):             0.0458
## I^2 (residual heterogeneity / unaccounted variability): 96.80%
## H^2 (unaccounted variability / sampling variability):   31.22
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 31.2172, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8513, p-val = 0.3562
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1444  0.0507  -2.8492  0.0044  -0.2438  -0.0451  ** 
## continentEurope   -0.0558  0.0604  -0.9226  0.3562  -0.1742   0.0627     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.5625   -5.1250    0.8750   -5.1250   24.8750   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0006)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8746, p-val = 0.3497
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6659, p-val = 0.4145
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.4115  0.0606  -6.7919  <.0001  -0.5302  -0.2927  *** 
## continentEurope    0.0503  0.0616   0.8160  0.4145  -0.0705   0.1711      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9403, p-val = 0.3322
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.7445, p-val = 0.0530
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0156  0.0279  -0.5595  0.5758  -0.0702  0.0390    
## continentEurope    0.0554  0.0286   1.9351  0.0530  -0.0007  0.1115  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.9055   -3.8110    2.1890   -3.8110   26.1890   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0012 (SE = 0.0018)
## tau (square root of estimated tau^2 value):             0.0347
## I^2 (residual heterogeneity / unaccounted variability): 92.95%
## H^2 (unaccounted variability / sampling variability):   14.18
## R^2 (amount of heterogeneity accounted for):            38.78%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.1799, p-val = 0.0002
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0710, p-val = 0.1501
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.4170  0.1626  -2.5654  0.0103  -0.7357  -0.0984  * 
## mean.age    0.0042  0.0029   1.4391  0.1501  -0.0015   0.0099    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.1499   -4.2999    1.7001   -4.2999   25.7001   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0011)
## tau (square root of estimated tau^2 value):             0.0157
## I^2 (residual heterogeneity / unaccounted variability): 31.02%
## H^2 (unaccounted variability / sampling variability):   1.45
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4497, p-val = 0.2286
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2150, p-val = 0.6429
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.2770  0.1833  -1.5113  0.1307  -0.6363  0.0822    
## mean.age   -0.0016  0.0036  -0.4637  0.6429  -0.0086  0.0053    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0003 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0171
## I^2 (residual heterogeneity / unaccounted variability): 61.44%
## H^2 (unaccounted variability / sampling variability):   2.59
## R^2 (amount of heterogeneity accounted for):            19.04%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5933, p-val = 0.1073
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0246, p-val = 0.1548
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.1919  0.1119   1.7139  0.0865  -0.0275  0.4112  . 
## mean.age   -0.0030  0.0021  -1.4229  0.1548  -0.0071  0.0011    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.3573   -2.7146    3.2854   -2.7146   27.2854   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0036 (SE = 0.0055)
## tau (square root of estimated tau^2 value):             0.0602
## I^2 (residual heterogeneity / unaccounted variability): 93.59%
## H^2 (unaccounted variability / sampling variability):   15.59
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.5935, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1044, p-val = 0.7466
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.1240  0.1849  -0.6706  0.5025  -0.4865  0.2385    
## scale1    -0.0061  0.0188  -0.3231  0.7466  -0.0430  0.0308    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.9466   -3.8933    2.1067   -3.8933   26.1067   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0027)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.5265, p-val = 0.4681
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0140, p-val = 0.3139
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.2910  0.0723  -4.0269  <.0001  -0.4326  -0.1494  *** 
## scale1    -0.0070  0.0070  -1.0070  0.3139  -0.0207   0.0067      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.8771   -3.7542    2.2458   -3.7542   26.2458   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0010 (SE = 0.0019)
## tau (square root of estimated tau^2 value):             0.0309
## I^2 (residual heterogeneity / unaccounted variability): 69.74%
## H^2 (unaccounted variability / sampling variability):   3.31
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.3052, p-val = 0.0691
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6957, p-val = 0.4042
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    0.1155  0.1046   1.1045  0.2694  -0.0895  0.3206    
## scale1    -0.0089  0.0107  -0.8341  0.4042  -0.0299  0.0120    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.5. Occupational risk-taking

Intercept only model

Meta analysis
ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.7913   -5.5827   -1.5827   -4.1964   10.4173   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0036)
## tau (square root of estimated tau^2 value):      0.0594
## I^2 (total heterogeneity / total variability):   98.37%
## H^2 (total variability / sampling variability):  61.20
## 
## Test for Heterogeneity:
## Q(df = 2) = 143.2600, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4097  0.0347  11.8206  <.0001  0.3417  0.4776  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.1260   -2.2520    3.7480   -2.2520   27.7480   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0061 (SE = 0.0087)
## tau (square root of estimated tau^2 value):             0.0782
## I^2 (residual heterogeneity / unaccounted variability): 99.29%
## H^2 (unaccounted variability / sampling variability):   141.21
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 141.2076, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1596, p-val = 0.6895
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt            0.4355  0.0790   5.5138  <.0001   0.2807  0.5903  *** 
## continentEurope   -0.0386  0.0965  -0.3995  0.6895  -0.2278  0.1506      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.0653   -2.1306    3.8694   -2.1306   27.8694   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0069 (SE = 0.0098)
## tau (square root of estimated tau^2 value):             0.0831
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability):   138.93
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 138.9308, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0282, p-val = 0.8666
## 
## Model Results:
## 
##           estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt     0.3505  0.3555  0.9860  0.3241  -0.3462  1.0472    
## mean.age    0.0011  0.0063  0.1680  0.8666  -0.0112  0.0133    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.0155   -6.0310   -0.0310   -6.0310   23.9690   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0081
## I^2 (residual heterogeneity / unaccounted variability): 46.85%
## H^2 (unaccounted variability / sampling variability):   1.88
## R^2 (amount of heterogeneity accounted for):            98.13%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8815, p-val = 0.1702
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 58.9630, p-val < .0001
## 
## Model Results:
## 
##          estimate      se    zval    pval   ci.lb   ci.ub 
## intrcpt    0.1567  0.0339  4.6265  <.0001  0.0903  0.2231  *** 
## scale1     0.0264  0.0034  7.6787  <.0001  0.0196  0.0331  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   5.0642  -10.1284   -6.1284   -8.7421    5.8716   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0180
## I^2 (total heterogeneity / total variability):   83.94%
## H^2 (total variability / sampling variability):  6.23
## 
## Test for Heterogeneity:
## Q(df = 2) = 16.3123, p-val = 0.0003
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1747  0.0117  -14.9541  <.0001  -0.1976  -0.1518  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.2959   -4.5917    1.4083   -4.5917   25.4083   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0235
## I^2 (residual heterogeneity / unaccounted variability): 93.33%
## H^2 (unaccounted variability / sampling variability):   14.99
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.9881, p-val = 0.0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0666, p-val = 0.7964
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1680  0.0279  -6.0127  <.0001  -0.2228  -0.1133  *** 
## continentEurope   -0.0085  0.0328  -0.2581  0.7964  -0.0728   0.0559      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.3771   -4.7541    1.2459   -4.7541   25.2459   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0213
## I^2 (residual heterogeneity / unaccounted variability): 89.95%
## H^2 (unaccounted variability / sampling variability):   9.95
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.9494, p-val = 0.0016
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2827, p-val = 0.5950
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.2283  0.1024  -2.2301  0.0257  -0.4290  -0.0277  * 
## mean.age    0.0010  0.0018   0.5317  0.5950  -0.0026   0.0046    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.6155   -5.2309    0.7691   -5.2309   24.7691   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value):             0.0138
## I^2 (residual heterogeneity / unaccounted variability): 61.08%
## H^2 (unaccounted variability / sampling variability):   2.57
## R^2 (amount of heterogeneity accounted for):            41.09%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5691, p-val = 0.1090
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7407, p-val = 0.1870
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1129  0.0484  -2.3303  0.0198  -0.2078  -0.0179  * 
## scale1    -0.0065  0.0050  -1.3194  0.1870  -0.0162   0.0032    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   5.1853  -10.3707   -6.3707   -8.9844    5.6293   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0167
## I^2 (total heterogeneity / total variability):   81.87%
## H^2 (total variability / sampling variability):  5.52
## 
## Test for Heterogeneity:
## Q(df = 2) = 14.2366, p-val = 0.0008
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1747  0.0110  -15.8989  <.0001  -0.1963  -0.1532  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.3642   -4.7284    1.2716   -4.7284   25.2716   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0219
## I^2 (residual heterogeneity / unaccounted variability): 92.36%
## H^2 (unaccounted variability / sampling variability):   13.08
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.0828, p-val = 0.0003
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0655, p-val = 0.7980
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1684  0.0265  -6.3510  <.0001  -0.2203  -0.1164  *** 
## continentEurope   -0.0079  0.0310  -0.2559  0.7980  -0.0687   0.0528      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.4448   -4.8897    1.1103   -4.8897   25.1103   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value):             0.0197
## I^2 (residual heterogeneity / unaccounted variability): 88.52%
## H^2 (unaccounted variability / sampling variability):   8.71
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.7102, p-val = 0.0032
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2838, p-val = 0.5942
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.2253  0.0964  -2.3363  0.0195  -0.4143  -0.0363  * 
## mean.age    0.0009  0.0017   0.5328  0.5942  -0.0025   0.0043    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.6867   -5.3734    0.6266   -5.3734   24.6266   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value):             0.0123
## I^2 (residual heterogeneity / unaccounted variability): 55.59%
## H^2 (unaccounted variability / sampling variability):   2.25
## R^2 (amount of heterogeneity accounted for):            46.03%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2516, p-val = 0.1335
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8955, p-val = 0.1686
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1158  0.0444  -2.6084  0.0091  -0.2027  -0.0288  ** 
## scale1    -0.0062  0.0045  -1.3768  0.1686  -0.0151   0.0026     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   5.3576  -10.7151   -6.7151   -9.3288    5.2849   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0003)
## tau (square root of estimated tau^2 value):      0.0151
## I^2 (total heterogeneity / total variability):   79.07%
## H^2 (total variability / sampling variability):  4.78
## 
## Test for Heterogeneity:
## Q(df = 2) = 12.0238, p-val = 0.0024
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1793  0.0101  -17.7267  <.0001  -0.1992  -0.1595  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.7595   -5.5189   -1.5189   -4.1326   10.4811   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value):      0.0560
## I^2 (total heterogeneity / total variability):   87.81%
## H^2 (total variability / sampling variability):  8.21
## 
## Test for Heterogeneity:
## Q(df = 2) = 14.3779, p-val = 0.0008
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2714  0.0347  -7.8218  <.0001  -0.3394  -0.2034  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.4320   -4.8639    1.1361   -4.8639   25.1361   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value):             0.0203
## I^2 (residual heterogeneity / unaccounted variability): 91.41%
## H^2 (unaccounted variability / sampling variability):   11.65
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 11.6481, p-val = 0.0006
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0018, p-val = 0.9660
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1780  0.0251  -7.0862  <.0001  -0.2272  -0.1287  *** 
## continentEurope   -0.0012  0.0293  -0.0427  0.9660  -0.0586   0.0561      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.2818   -2.5636    3.4364   -2.5636   27.4364   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value):             0.0644
## I^2 (residual heterogeneity / unaccounted variability): 91.90%
## H^2 (unaccounted variability / sampling variability):   12.35
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.3468, p-val = 0.0004
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6069, p-val = 0.4360
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.3165  0.0700  -4.5214  <.0001  -0.4536  -0.1793  *** 
## continentEurope    0.0659  0.0846   0.7790  0.4360  -0.0999   0.2316      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.4590   -4.9179    1.0821   -4.9179   25.0821   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value):             0.0195
## I^2 (residual heterogeneity / unaccounted variability): 88.42%
## H^2 (unaccounted variability / sampling variability):   8.64
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6391, p-val = 0.0033
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0849, p-val = 0.7708
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.2064  0.0950  -2.1725  0.0298  -0.3926  -0.0202  * 
## mean.age    0.0005  0.0017   0.2913  0.7708  -0.0029   0.0039    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.1402   -2.2803    3.7197   -2.2803   27.7197   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0056 (SE = 0.0085)
## tau (square root of estimated tau^2 value):             0.0746
## I^2 (residual heterogeneity / unaccounted variability): 92.92%
## H^2 (unaccounted variability / sampling variability):   14.12
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.1244, p-val = 0.0002
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2355, p-val = 0.6275
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1120  0.3313  -0.3381  0.7353  -0.7613  0.5373    
## mean.age   -0.0028  0.0059  -0.4853  0.6275  -0.0143  0.0086    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.0928   -6.1857   -0.1857   -6.1857   23.8143   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0019
## I^2 (residual heterogeneity / unaccounted variability): 3.10%
## H^2 (unaccounted variability / sampling variability):   1.03
## R^2 (amount of heterogeneity accounted for):            98.36%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0320, p-val = 0.3097
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 9.9312, p-val = 0.0016
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1130  0.0234  -4.8226  <.0001  -0.1590  -0.0671  *** 
## scale1    -0.0072  0.0023  -3.1514  0.0016  -0.0117  -0.0027   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.6951   -5.3901    0.6099   -5.3901   24.6099   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4535, p-val = 0.5007
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 13.9245, p-val = 0.0002
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.0283  0.0684  -0.4139  0.6790  -0.1623   0.1057      
## scale1    -0.0247  0.0066  -3.7316  0.0002  -0.0376  -0.0117  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender interaction model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   5.7723  -11.5446   -7.5446  -10.1583    4.4554   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0005
## I^2 (total heterogeneity / total variability):   0.21%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 2) = 2.1896, p-val = 0.3346
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1834  0.0050  -36.7338  <.0001  -0.1932  -0.1736  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.8349   -5.6697   -1.6697   -4.2834   10.3303   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0028 (SE = 0.0039)
## tau (square root of estimated tau^2 value):      0.0533
## I^2 (total heterogeneity / total variability):   81.59%
## H^2 (total variability / sampling variability):  5.43
## 
## Test for Heterogeneity:
## Q(df = 2) = 13.8097, p-val = 0.0010
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2494  0.0360  -6.9232  <.0001  -0.3200  -0.1788  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.6823   -7.3645   -3.3645   -5.9782    8.6355   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0014)
## tau (square root of estimated tau^2 value):      0.0319
## I^2 (total heterogeneity / total variability):   80.61%
## H^2 (total variability / sampling variability):  5.16
## 
## Test for Heterogeneity:
## Q(df = 2) = 9.3161, p-val = 0.0095
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0030  0.0211  -0.1422  0.8869  -0.0444  0.0384    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.6864   -7.3729   -1.3729   -7.3729   22.6271   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0001)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.3067, p-val = 0.5797
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8829, p-val = 0.1700
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.1525  0.0231  -6.5998  <.0001  -0.1978  -0.1072  *** 
## continentEurope   -0.0325  0.0237  -1.3722  0.1700  -0.0788   0.0139      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.2385   -2.4770    3.5230   -2.4770   27.5230   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0046 (SE = 0.0070)
## tau (square root of estimated tau^2 value):             0.0675
## I^2 (residual heterogeneity / unaccounted variability): 92.53%
## H^2 (unaccounted variability / sampling variability):   13.39
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.3916, p-val = 0.0003
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0275, p-val = 0.8683
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.2344  0.0926  -2.5302  0.0114  -0.4160  -0.0528  * 
## continentEurope   -0.0174  0.1051  -0.1659  0.8683  -0.2233   0.1885    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value):             0.0322
## I^2 (residual heterogeneity / unaccounted variability): 86.94%
## H^2 (unaccounted variability / sampling variability):   7.66
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.6565, p-val = 0.0057
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0764, p-val = 0.2995
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0430  0.0440  -0.9770  0.3286  -0.1293  0.0433    
## continentEurope    0.0522  0.0503   1.0375  0.2995  -0.0464  0.1508    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.7003   -7.4006   -1.4006   -7.4006   22.5994   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0001)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0044, p-val = 0.9471
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.1852, p-val = 0.1393
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    -0.2779  0.0641  -4.3366  <.0001  -0.4034  -0.1523  *** 
## mean.age    0.0019  0.0013   1.4782  0.1393  -0.0006   0.0043      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.3012   -2.6023    3.3977   -2.6023   27.3977   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0038 (SE = 0.0061)
## tau (square root of estimated tau^2 value):             0.0618
## I^2 (residual heterogeneity / unaccounted variability): 87.94%
## H^2 (unaccounted variability / sampling variability):   8.29
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.2948, p-val = 0.0040
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2076, p-val = 0.6487
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.3963  0.3271  -1.2117  0.2256  -1.0373  0.2447    
## mean.age    0.0027  0.0060   0.4556  0.6487  -0.0090  0.0144    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0017 (SE = 0.0026)
## tau (square root of estimated tau^2 value):             0.0407
## I^2 (residual heterogeneity / unaccounted variability): 89.24%
## H^2 (unaccounted variability / sampling variability):   9.29
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.2935, p-val = 0.0023
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4640, p-val = 0.4957
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.1293  0.1974   0.6552  0.5123  -0.2575  0.5162    
## mean.age   -0.0024  0.0036  -0.6812  0.4957  -0.0094  0.0045    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.3089   -4.6179    1.3821   -4.6179   25.3821   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0003 (SE = 0.0008)
## tau (square root of estimated tau^2 value):             0.0172
## I^2 (residual heterogeneity / unaccounted variability): 50.92%
## H^2 (unaccounted variability / sampling variability):   2.04
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0375, p-val = 0.1535
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0134, p-val = 0.9080
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.1849  0.0617  -2.9980  0.0027  -0.3058  -0.0640  ** 
## scale1     0.0007  0.0063   0.1156  0.9080  -0.0117   0.0132     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6602   -3.3204    2.6796   -3.3204   26.6796   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0000 (SE = 0.0030)
## tau (square root of estimated tau^2 value):             0.0037
## I^2 (residual heterogeneity / unaccounted variability): 0.66%
## H^2 (unaccounted variability / sampling variability):   1.01
## R^2 (amount of heterogeneity accounted for):            99.51%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0066, p-val = 0.3157
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 12.2996, p-val = 0.0005
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.0317  0.0700  -0.4535  0.6502  -0.1689   0.1054      
## scale1    -0.0241  0.0069  -3.5071  0.0005  -0.0375  -0.0106  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.4443   -4.8886    1.1114   -4.8886   25.1114   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9106, p-val = 0.3400
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.4055, p-val = 0.0037
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    0.1244  0.0453   2.7456  0.0060   0.0356   0.2132  ** 
## scale1    -0.0127  0.0044  -2.8992  0.0037  -0.0213  -0.0041  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.6. Health risk-taking

Intercept only model

Meta analysis
ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.2938   -6.5876   -2.5876   -5.2013    9.4124   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0021 (SE = 0.0022)
## tau (square root of estimated tau^2 value):      0.0459
## I^2 (total heterogeneity / total variability):   97.34%
## H^2 (total variability / sampling variability):  37.57
## 
## Test for Heterogeneity:
## Q(df = 2) = 86.4426, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.3790  0.0270  14.0430  <.0001  0.3261  0.4319  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.4510   -2.9020    3.0980   -2.9020   27.0980   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0032 (SE = 0.0045)
## tau (square root of estimated tau^2 value):             0.0564
## I^2 (residual heterogeneity / unaccounted variability): 98.81%
## H^2 (unaccounted variability / sampling variability):   84.20
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 84.1991, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3303, p-val = 0.5655
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt            0.4064  0.0579   7.0201  <.0001   0.2930  0.5199  *** 
## continentEurope   -0.0405  0.0704  -0.5747  0.5655  -0.1785  0.0976      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.3544   -2.7089    3.2911   -2.7089   27.2911   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0039 (SE = 0.0055)
## tau (square root of estimated tau^2 value):             0.0621
## I^2 (residual heterogeneity / unaccounted variability): 98.80%
## H^2 (unaccounted variability / sampling variability):   83.27
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 83.2654, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1009, p-val = 0.7507
## 
## Model Results:
## 
##           estimate      se    zval    pval    ci.lb   ci.ub 
## intrcpt     0.2949  0.2675  1.1024  0.2703  -0.2294  0.8193    
## mean.age    0.0015  0.0047  0.3177  0.7507  -0.0077  0.0107    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
ICC’s results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.7004   -7.4008   -1.4008   -7.4008   22.5992   
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0001)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0020, p-val = 0.9647
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 86.4407, p-val < .0001
## 
## Model Results:
## 
##          estimate      se    zval    pval   ci.lb   ci.ub 
## intrcpt    0.1852  0.0220  8.3995  <.0001  0.1420  0.2284  *** 
## scale1     0.0201  0.0022  9.2973  <.0001  0.0158  0.0243  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.6147   -7.2294   -3.2294   -5.8431    8.7706   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value):      0.0392
## I^2 (total heterogeneity / total variability):   96.96%
## H^2 (total variability / sampling variability):  32.93
## 
## Test for Heterogeneity:
## Q(df = 2) = 94.4415, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0841  0.0232  -3.6265  0.0003  -0.1295  -0.0386  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5085   -3.0171    2.9829   -3.0171   26.9829   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value):             0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.94%
## H^2 (unaccounted variability / sampling variability):   94.33
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 94.3332, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0694, p-val = 0.7921
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0960  0.0549  -1.7475  0.0805  -0.2037  0.0117  . 
## continentEurope    0.0176  0.0667   0.2635  0.7921  -0.1132  0.1483    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.4743   -2.9485    3.0515   -2.9485   27.0515   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value):             0.0550
## I^2 (residual heterogeneity / unaccounted variability): 98.72%
## H^2 (unaccounted variability / sampling variability):   78.29
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.2939, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0017, p-val = 0.9667
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0742  0.2381  -0.3118  0.7552  -0.5408  0.3923    
## mean.age   -0.0002  0.0042  -0.0417  0.9667  -0.0084  0.0081    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.8379   -5.6757    0.3243   -5.6757   24.3243   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value):             0.0102
## I^2 (residual heterogeneity / unaccounted variability): 51.53%
## H^2 (unaccounted variability / sampling variability):   2.06
## R^2 (amount of heterogeneity accounted for):            93.26%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0630, p-val = 0.1509
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 20.8789, p-val < .0001
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    0.0820  0.0374   2.1926  0.0283   0.0087   0.1554    * 
## scale1    -0.0175  0.0038  -4.5693  <.0001  -0.0250  -0.0100  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.5876   -7.1753   -3.1753   -5.7890    8.8247   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value):      0.0397
## I^2 (total heterogeneity / total variability):   97.06%
## H^2 (total variability / sampling variability):  33.99
## 
## Test for Heterogeneity:
## Q(df = 2) = 97.5929, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0847  0.0235  -3.6052  0.0003  -0.1307  -0.0386  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.4936   -2.9871    3.0129   -2.9871   27.0129   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0029 (SE = 0.0042)
## tau (square root of estimated tau^2 value):             0.0541
## I^2 (residual heterogeneity / unaccounted variability): 98.97%
## H^2 (unaccounted variability / sampling variability):   97.46
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 97.4647, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0675, p-val = 0.7951
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0966  0.0557  -1.7342  0.0829  -0.2058  0.0126  . 
## continentEurope    0.0176  0.0677   0.2597  0.7951  -0.1151  0.1502    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.4601   -2.9203    3.0797   -2.9203   27.0797   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0031 (SE = 0.0045)
## tau (square root of estimated tau^2 value):             0.0558
## I^2 (residual heterogeneity / unaccounted variability): 98.76%
## H^2 (unaccounted variability / sampling variability):   80.83
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 80.8303, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0015, p-val = 0.9695
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0755  0.2413  -0.3130  0.7543  -0.5486  0.3975    
## mean.age   -0.0002  0.0043  -0.0383  0.9695  -0.0085  0.0082    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.8094   -5.6189    0.3811   -5.6189   24.3811   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value):             0.0108
## I^2 (residual heterogeneity / unaccounted variability): 54.67%
## H^2 (unaccounted variability / sampling variability):   2.21
## R^2 (amount of heterogeneity accounted for):            92.64%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2059, p-val = 0.1375
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 19.6293, p-val < .0001
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    0.0832  0.0390   2.1360  0.0327   0.0069   0.1596    * 
## scale1    -0.0177  0.0040  -4.4305  <.0001  -0.0255  -0.0098  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.6265   -7.2531   -3.2531   -5.8668    8.7469   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value):      0.0389
## I^2 (total heterogeneity / total variability):   96.99%
## H^2 (total variability / sampling variability):  33.21
## 
## Test for Heterogeneity:
## Q(df = 2) = 92.6322, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0871  0.0230  -3.7846  0.0002  -0.1322  -0.0420  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.2291   -6.4582   -2.4582   -5.0719    9.5418   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0024)
## tau (square root of estimated tau^2 value):      0.0443
## I^2 (total heterogeneity / total variability):   84.02%
## H^2 (total variability / sampling variability):  6.26
## 
## Test for Heterogeneity:
## Q(df = 2) = 13.8450, p-val = 0.0010
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2503  0.0281  -8.9043  <.0001  -0.3054  -0.1952  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5270   -3.0541    2.9459   -3.0541   26.9459   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0027 (SE = 0.0039)
## tau (square root of estimated tau^2 value):             0.0523
## I^2 (residual heterogeneity / unaccounted variability): 98.92%
## H^2 (unaccounted variability / sampling variability):   92.62
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 92.6211, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0983, p-val = 0.7538
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.1011  0.0539  -1.8741  0.0609  -0.2068  0.0046  . 
## continentEurope    0.0205  0.0655   0.3136  0.7538  -0.1078  0.1489    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.2976   -2.5951    3.4049   -2.5951   27.4049   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0041 (SE = 0.0062)
## tau (square root of estimated tau^2 value):             0.0636
## I^2 (residual heterogeneity / unaccounted variability): 92.69%
## H^2 (unaccounted variability / sampling variability):   13.68
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.6766, p-val = 0.0002
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0228, p-val = 0.8801
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.2578  0.0684  -3.7680  0.0002  -0.3919  -0.1237  *** 
## continentEurope    0.0125  0.0828   0.1509  0.8801  -0.1499   0.1749      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.4824   -2.9648    3.0352   -2.9648   27.0352   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value):             0.0546
## I^2 (residual heterogeneity / unaccounted variability): 98.73%
## H^2 (unaccounted variability / sampling variability):   78.55
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.5499, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0078, p-val = 0.9296
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.0665  0.2361  -0.2816  0.7783  -0.5292  0.3962    
## mean.age   -0.0004  0.0042  -0.0884  0.9296  -0.0085  0.0078    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.2828   -2.5655    3.4345   -2.5655   27.4345   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value):             0.0643
## I^2 (residual heterogeneity / unaccounted variability): 91.77%
## H^2 (unaccounted variability / sampling variability):   12.15
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.1479, p-val = 0.0005
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0045, p-val = 0.9467
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.2683  0.2871  -0.9345  0.3501  -0.8309  0.2944    
## mean.age    0.0003  0.0051   0.0668  0.9467  -0.0096  0.0103    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.0391   -6.0782   -0.0782   -6.0782   23.9218   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0.0063
## I^2 (residual heterogeneity / unaccounted variability): 29.89%
## H^2 (unaccounted variability / sampling variability):   1.43
## R^2 (amount of heterogeneity accounted for):            97.35%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4264, p-val = 0.2323
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 38.9987, p-val < .0001
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt    0.0814  0.0281   2.8905  0.0038   0.0262   0.1365   ** 
## scale1    -0.0178  0.0028  -6.2449  <.0001  -0.0234  -0.0122  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.3580   -4.7159    1.2841   -4.7159   25.2841   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0001 (SE = 0.0007)
## tau (square root of estimated tau^2 value):             0.0117
## I^2 (residual heterogeneity / unaccounted variability): 25.98%
## H^2 (unaccounted variability / sampling variability):   1.35
## R^2 (amount of heterogeneity accounted for):            93.07%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.3510, p-val = 0.2451
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.6026, p-val = 0.0034
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt   -0.0483  0.0735  -0.6572  0.5110  -0.1923   0.0957     
## scale1    -0.0212  0.0072  -2.9330  0.0034  -0.0354  -0.0070  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear with gender interaction model

Meta analysis
Age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.7059   -7.4117   -3.4117   -6.0254    8.5883   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0015)
## tau (square root of estimated tau^2 value):      0.0370
## I^2 (total heterogeneity / total variability):   93.41%
## H^2 (total variability / sampling variability):  15.17
## 
## Test for Heterogeneity:
## Q(df = 2) = 45.6253, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0958  0.0226  -4.2301  <.0001  -0.1401  -0.0514  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.8847   -5.7694   -1.7694   -4.3831   10.2306   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0026 (SE = 0.0036)
## tau (square root of estimated tau^2 value):      0.0509
## I^2 (total heterogeneity / total variability):   81.98%
## H^2 (total variability / sampling variability):  5.55
## 
## Test for Heterogeneity:
## Q(df = 2) = 13.4925, p-val = 0.0012
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2456  0.0343  -7.1626  <.0001  -0.3128  -0.1784  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.9531   -7.9061   -3.9061   -6.5198    8.0939   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0008)
## tau (square root of estimated tau^2 value):      0.0227
## I^2 (total heterogeneity / total variability):   73.06%
## H^2 (total variability / sampling variability):  3.71
## 
## Test for Heterogeneity:
## Q(df = 2) = 5.2178, p-val = 0.0736
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0245  0.0158  1.5473  0.1218  -0.0065  0.0554    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with continent
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5745   -3.1489    2.8511   -3.1489   26.8511   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value):             0.0495
## I^2 (residual heterogeneity / unaccounted variability): 97.68%
## H^2 (unaccounted variability / sampling variability):   43.19
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 43.1890, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0509, p-val = 0.8214
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0851  0.0539  -1.5777  0.1146  -0.1907  0.0206    
## continentEurope   -0.0146  0.0645  -0.2257  0.8214  -0.1410  0.1119    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.3471   -2.6943    3.3057   -2.6943   27.3057   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0036 (SE = 0.0056)
## tau (square root of estimated tau^2 value):             0.0603
## I^2 (residual heterogeneity / unaccounted variability): 91.75%
## H^2 (unaccounted variability / sampling variability):   12.12
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.1193, p-val = 0.0005
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2574, p-val = 0.6119
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt           -0.2057  0.0848  -2.4249  0.0153  -0.3719  -0.0394  * 
## continentEurope   -0.0486  0.0958  -0.5073  0.6119  -0.2363   0.1391    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0002)
## tau (square root of estimated tau^2 value):             0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability):   1.00
## R^2 (amount of heterogeneity accounted for):            100.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4999, p-val = 0.4795
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.7179, p-val = 0.0299
## 
## Model Results:
## 
##                  estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt           -0.0262  0.0272  -0.9626  0.3357  -0.0795  0.0271    
## continentEurope    0.0606  0.0279   2.1721  0.0299   0.0059  0.1153  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with mean age
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.6484   -3.2968    2.7032   -3.2968   26.7032   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value):             0.0457
## I^2 (residual heterogeneity / unaccounted variability): 96.41%
## H^2 (unaccounted variability / sampling variability):   27.88
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 27.8790, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2292, p-val = 0.6321
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.1929  0.2056  -0.9382  0.3481  -0.5958  0.2101    
## mean.age    0.0018  0.0037   0.4787  0.6321  -0.0054  0.0089    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.5103   -3.0205    2.9795   -3.0205   26.9795   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0024 (SE = 0.0040)
## tau (square root of estimated tau^2 value):             0.0489
## I^2 (residual heterogeneity / unaccounted variability): 83.68%
## H^2 (unaccounted variability / sampling variability):   6.13
## R^2 (amount of heterogeneity accounted for):            7.74%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 6.1268, p-val = 0.0133
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7924, p-val = 0.3734
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    -0.4901  0.2762  -1.7744  0.0760  -1.0315  0.0513  . 
## mean.age    0.0045  0.0051   0.8902  0.3734  -0.0054  0.0145    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0002 (SE = 0.0005)
## tau (square root of estimated tau^2 value):             0.0138
## I^2 (residual heterogeneity / unaccounted variability): 55.26%
## H^2 (unaccounted variability / sampling variability):   2.23
## R^2 (amount of heterogeneity accounted for):            62.87%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2349, p-val = 0.1349
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8308, p-val = 0.0925
## 
## Model Results:
## 
##           estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt     0.1966  0.1009   1.9486  0.0513  -0.0011  0.3943  . 
## mean.age   -0.0032  0.0019  -1.6825  0.0925  -0.0069  0.0005  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis with scale
Age effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.9326   -3.8653    2.1347   -3.8653   26.1347   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value):             0.0314
## I^2 (residual heterogeneity / unaccounted variability): 80.51%
## H^2 (unaccounted variability / sampling variability):   5.13
## R^2 (amount of heterogeneity accounted for):            27.85%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 5.1296, p-val = 0.0235
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5190, p-val = 0.2178
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    0.0245  0.0999   0.2454  0.8061  -0.1713  0.2203    
## scale1    -0.0126  0.0102  -1.2325  0.2178  -0.0326  0.0074    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.3241   -2.6482    3.3518   -2.6482   27.3518   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0023 (SE = 0.0059)
## tau (square root of estimated tau^2 value):             0.0478
## I^2 (residual heterogeneity / unaccounted variability): 55.24%
## H^2 (unaccounted variability / sampling variability):   2.23
## R^2 (amount of heterogeneity accounted for):            11.62%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2340, p-val = 0.1350
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8570, p-val = 0.3546
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt   -0.0989  0.1625  -0.6085  0.5429  -0.4174  0.2196    
## scale1    -0.0156  0.0168  -0.9258  0.3546  -0.0485  0.0174    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Age \(\times\) Gender effect results
## 
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   1.7678   -3.5355    2.4645   -3.5355   26.4645   
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0013 (SE = 0.0024)
## tau (square root of estimated tau^2 value):             0.0362
## I^2 (residual heterogeneity / unaccounted variability): 76.89%
## H^2 (unaccounted variability / sampling variability):   4.33
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.3264, p-val = 0.0375
## 
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5037, p-val = 0.4779
## 
## Model Results:
## 
##          estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt    0.1029  0.1177   0.8744  0.3819  -0.1278  0.3337    
## scale1    -0.0085  0.0120  -0.7097  0.4779  -0.0321  0.0151    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.7. Social risk-taking

6. Meta analytic summary of slope estimates

Intercept-only model

Fixed effect model

Linear model

Linear with gender model

Linear with gender interaction model

Quadratic model

Quadratic with gender model